Index (Deep Netts Core Pro 3.2.0 API)

A B C D E F G H I K L M N O P R S T U V W X Z
All Classes and Interfaces|All Packages|Constant Field Values|Serialized Form

A

absMax(float[], float[]) - Static method in class deepnetts.tensor.Tensors

Returns array with max values for each position in the given input vectors.

absMax(TensorBase, TensorBase) - Static method in class deepnetts.tensor.Tensors

Returns tensors with max value for each component of input tensors.

absMin(float[], float[]) - Static method in class deepnetts.tensor.Tensors

absMin(TensorBase, TensorBase) - Static method in class deepnetts.tensor.Tensors

absPrime(float) - Static method in class deepnetts.net.layers.activation.MathFunctions

First derivative of the abs function.

AbstractBackwardCuda - Class in deepnetts.cudnn

AbstractBackwardCuda(CudaHandles) - Constructor for class deepnetts.cudnn.AbstractBackwardCuda

AbstractBackwardCuda(CudaHandles, AbstractLayer) - Constructor for class deepnetts.cudnn.AbstractBackwardCuda

AbstractForwardCuda - Class in deepnetts.cudnn

AbstractForwardCuda(CudaHandles) - Constructor for class deepnetts.cudnn.AbstractForwardCuda

AbstractForwardCuda(CudaHandles, AbstractLayer) - Constructor for class deepnetts.cudnn.AbstractForwardCuda

AbstractLayer<I,O,W> - Class in deepnetts.net.layers

Base class for different types of layers.

AbstractLayer(ActivationType) - Constructor for class deepnetts.net.layers.AbstractLayer

AbstractOptimizer - Class in deepnetts.net.train.opt

Skeletal implementation of the Optimizer interface to minimize effort to implement specific optimizers.

AbstractOptimizer(AbstractLayer) - Constructor for class deepnetts.net.train.opt.AbstractOptimizer

AbstractScaler - Class in deepnetts.data.norm

Base class to simplify implementation of custom normalization procedure.

AbstractScaler() - Constructor for class deepnetts.data.norm.AbstractScaler

accept(int, float[], float[]) - Method in interface deepnetts.util.ConvolutionalChannelConsumer

accept(int, int) - Method in interface deepnetts.util.RangeConsumer

accept(TensorBase) - Method in interface deepnetts.net.layers.activation.ActivationFunction

accuracy - Variable in class deepnetts.eval.ClassificationMetrics.Stats

ActivationFunction - Interface in deepnetts.net.layers.activation

Common base interface for all activation functions used in layers.

ActivationType - Enum Class in deepnetts.net.layers.activation

Supported types of activation functions.

ADADELTA - Enum constant in enum class deepnetts.net.train.opt.OptimizerType

AdaDeltaOptimizer - Class in deepnetts.net.train.opt

Implementation of ADADELTA Optimizer which is a modification od

invalid reference

AdaGrad

that uses only a limited window or previous gradients.

AdaDeltaOptimizer(AbstractLayer) - Constructor for class deepnetts.net.train.opt.AdaDeltaOptimizer

ADAGRAD - Enum constant in enum class deepnetts.net.train.opt.OptimizerType

AdaGradOptimizer - Class in deepnetts.net.train.opt

Implementation of ADAGRAD Optimizer , which uses sum of squared previous gradients to adjust a global learning rate for each weight.

AdaGradOptimizer(AbstractLayer) - Constructor for class deepnetts.net.train.opt.AdaGradOptimizer

ADAM - Enum constant in enum class deepnetts.net.train.opt.OptimizerType

AdamOptimizer - Class in deepnetts.net.train.opt

Implementation of Adam optimizer which is a variation of RmsProp which includes momentum-like factor.

AdamOptimizer(AbstractLayer) - Constructor for class deepnetts.net.train.opt.AdamOptimizer

add(float) - Method in class deepnetts.tensor.TensorBase

add(float[], float[]) - Method in class deepnetts.eval.RootMeanSquaredError

add(float[], float[]) - Static method in class deepnetts.tensor.Tensors

Adds given vector and store result in first.

add(float, int) - Method in class deepnetts.tensor.Tensor1D

Adds specified value to an element at specified index position in tensor.

add(float, int, int) - Method in class deepnetts.tensor.Tensor2D

Adds specified value to matrix value at position x, y

add(float, int, int, int) - Method in class deepnetts.tensor.Tensor3D

add(float, int, int, int, int) - Method in class deepnetts.tensor.Tensor4D

add(Parameter) - Method in class deepnetts.automl.Parameters

add(ExampleImage) - Method in class deepnetts.data.ImageSet

Adds an example image that will be used to train deep learning model to this set.

add(Tensor1D) - Method in class deepnetts.tensor.Tensor2D

add(TensorBase) - Method in class deepnetts.tensor.TensorBase

Adds specified tensor t to this tensor.

add(String, List<T>) - Method in class deepnetts.automl.Parameters

Adds a list of possible values for specified parameter name

add(String, T...) - Method in class deepnetts.automl.Parameters

addConvolutionalLayer(int) - Method in class deepnetts.net.ConvolutionalNetwork.Builder

Adds a convolutional layer with the given number of channels(filters), with default 3x3 filter size and default activation function.

addConvolutionalLayer(int, int) - Method in class deepnetts.net.ConvolutionalNetwork.Builder

Adds a convolutional layer with the given number of channels(filters), with given filter size (same width and height) and default activation function.

addConvolutionalLayer(int, int, int) - Method in class deepnetts.net.ConvolutionalNetwork.Builder

Adds a convolutional layer with the given number of channels(filters), with given width and height of convolutional filter and default type of activation function.

addConvolutionalLayer(int, int, int, int) - Method in class deepnetts.net.ConvolutionalNetwork.Builder

Adds a convolutional layer with the given width and height of convolutional filters, given number of channels(filters) and stride(filter step), and default type of activation function.

addConvolutionalLayer(int, int, int, int, ActivationType) - Method in class deepnetts.net.ConvolutionalNetwork.Builder

Adds a convolutional layer with the given width and height of convolutional filters, given number of channels(filters) and stride(filter step), and given type of activation function.

addConvolutionalLayer(int, int, ActivationType) - Method in class deepnetts.net.ConvolutionalNetwork.Builder

Adds a convolutional layer with the given number of channels(filters), with given filter size (same width and height) and given type of activation function.

addConvolutionalLayer(int, ActivationType) - Method in class deepnetts.net.ConvolutionalNetwork.Builder

Adds a convolutional layer with the given number of channels(filters), with given activation function type and default 3x3 filter size.

addConvolutionalLayer(int, Filter) - Method in class deepnetts.net.ConvolutionalNetwork.Builder

Adds a convolutional layer with the given number of channels(filters), with given settings of a convolutional filter and default type of activation function.

addConvolutionalLayer(int, Filter, ActivationType) - Method in class deepnetts.net.ConvolutionalNetwork.Builder

Adds a convolutional layer with the given number of channels(filters), with given settings of a convolutional filter and given type of activation function.

addFullyConnectedLayer(int) - Method in class deepnetts.net.ConvolutionalNetwork.Builder

Adds fully connected layer with specified width and default activation function.

addFullyConnectedLayer(int) - Method in class deepnetts.net.FeedForwardNetwork.Builder

Adds to the network a fully connected layer with specified width and Relu activation function by default.

addFullyConnectedLayer(int, ActivationType) - Method in class deepnetts.net.ConvolutionalNetwork.Builder

Adds fully connected layer with specified width and activation function.

addFullyConnectedLayer(int, ActivationType) - Method in class deepnetts.net.FeedForwardNetwork.Builder

Adds fully connected addLayer with specified width and activation function to the network.

addHiddenFullyConnectedLayers(int...) - Method in class deepnetts.net.FeedForwardNetwork.Builder

Adds to the network several hidden fully connected layers with specified widths and default hidden activation function by default.

addHiddenFullyConnectedLayers(ActivationType, int...) - Method in class deepnetts.net.FeedForwardNetwork.Builder

Adds fully connected hidden layers with widths given in layerWidths param and given activation function type.

addInputLayer(int) - Method in class deepnetts.net.FeedForwardNetwork.Builder

Adds input layer with the specified layerWidth (number of inputs) to the network.

addInputLayer(int, int) - Method in class deepnetts.net.ConvolutionalNetwork.Builder

Input layer with specified width and height, and 3 channels by default.

addInputLayer(int, int) - Method in class deepnetts.net.FeedForwardNetwork.Builder

addInputLayer(int, int, int) - Method in class deepnetts.net.ConvolutionalNetwork.Builder

Input layer with specified width, height and number of channels (depth).

addInto(TensorBase, TensorBase) - Method in class deepnetts.tensor.TensorBase

addLayer(AbstractLayer) - Method in class deepnetts.net.ConvolutionalNetwork.Builder

Adds a given layer to the network.

addLayer(AbstractLayer) - Method in class deepnetts.net.FeedForwardNetwork.Builder

Adds custom layer to this network (which inherits from AbstractLayer)

addListener(TrainingListener) - Method in class deepnetts.net.train.BackpropagationTrainer

Adds training listener to this trainer.

addMaxPoolingLayer(int) - Method in class deepnetts.net.ConvolutionalNetwork.Builder

addMaxPoolingLayer(int, int) - Method in class deepnetts.net.ConvolutionalNetwork.Builder

Adds a max pooling layer with given filter size and stride(filter step).

addMaxPoolingLayer(int, int, int) - Method in class deepnetts.net.ConvolutionalNetwork.Builder

Adds a max pooling layer with given filter size and stride(filter step).Max pooling layer comes after convolutional layer and reduces the dimensions of the input received from the previous layer.Typically filter sizes of 2 are used, which effectively halves the dimensions of the input from the previous layer.

addMaxPoolingLayer(Filter) - Method in class deepnetts.net.ConvolutionalNetwork.Builder

Adds a max pooling layer with the given filter settings.Max pooling layer comes after convolutional layer and reduces the dimensions of the input received from the previous layer.

addOutputLayer(int, ActivationType) - Method in class deepnetts.net.ConvolutionalNetwork.Builder

Adds output layer to the neural network with specified width (number of outputs) and activation function type.

addOutputLayer(int, ActivationType) - Method in class deepnetts.net.FeedForwardNetwork.Builder

Adds output layer to the neural network with specified width (number of outputs) and activation function type.

addOutputLayer(int, Class<? extends OutputLayer>) - Method in class deepnetts.net.ConvolutionalNetwork.Builder

Adds output layer to the neural network with specified width (number of outputs) and layer class.

addPatternError(float[], float[]) - Method in class deepnetts.net.loss.BinaryCrossEntropyLoss

Calculates error for given actual and target patterns and adds that error to total error.

addPatternError(float[], float[]) - Method in class deepnetts.net.loss.CrossEntropyLoss

Calculates and returns outpurt error vector for specified predicted and target outputs.

addPatternError(float[], float[]) - Method in interface deepnetts.net.loss.LossFunction

Calculates pattern error for singe pattern for the specified predicted and target outputs, adds the error to total error, and returns the pattern error.

addPatternError(float[], float[]) - Method in class deepnetts.net.loss.MeanSquaredErrorLoss

Adds output error vector for the given predicted and target output vectors to total error sum and returns and error vector.

addPatternError(TensorBase, TensorBase) - Method in class deepnetts.net.loss.BinaryCrossEntropyLoss

addPatternError(TensorBase, TensorBase) - Method in class deepnetts.net.loss.CrossEntropyLoss

addPatternError(TensorBase, TensorBase) - Method in interface deepnetts.net.loss.LossFunction

addPatternError(TensorBase, TensorBase) - Method in class deepnetts.net.loss.MeanSquaredErrorLoss

addRegularizationSum(float) - Method in class deepnetts.net.loss.BinaryCrossEntropyLoss

addRegularizationSum(float) - Method in class deepnetts.net.loss.CrossEntropyLoss

addRegularizationSum(float) - Method in interface deepnetts.net.loss.LossFunction

Adds specified regularization sum to total loss.

addRegularizationSum(float) - Method in class deepnetts.net.loss.MeanSquaredErrorLoss

Add regularization sum to total loss

allocateAndCopyToDev() - Method in class deepnetts.cudnn.CudaTensorBridge

allocateOnDev() - Method in class deepnetts.cudnn.CudaTensorBridge

apply(ActivationFunction) - Method in class deepnetts.tensor.TensorBase

apply(Tensor3D, int) - Method in interface deepnetts.net.layers.activation.ActivationFunction

apply(Tensor3D, int) - Method in class deepnetts.net.layers.activation.LeakyRelu

apply(Tensor3D, int) - Method in class deepnetts.net.layers.activation.Linear

apply(Tensor3D, int) - Method in class deepnetts.net.layers.activation.Relu

apply(Tensor3D, int) - Method in class deepnetts.net.layers.activation.Sigmoid

apply(Tensor3D, int) - Method in class deepnetts.net.layers.activation.SoftSign

apply(Tensor3D, int) - Method in class deepnetts.net.layers.activation.Tanh

apply(TensorBase) - Method in class deepnetts.util.ImagePreprocessing

apply(TensorBase, int, int) - Method in interface deepnetts.net.layers.activation.ActivationFunction

apply(TensorBase, int, int) - Method in class deepnetts.net.layers.activation.LeakyRelu

apply(TensorBase, int, int) - Method in class deepnetts.net.layers.activation.Linear

apply(TensorBase, int, int) - Method in class deepnetts.net.layers.activation.Relu

apply(TensorBase, int, int) - Method in class deepnetts.net.layers.activation.Sigmoid

apply(TensorBase, int, int) - Method in class deepnetts.net.layers.activation.SoftSign

apply(TensorBase, int, int) - Method in class deepnetts.net.layers.activation.Tanh

apply(Function<Float, Float>) - Method in class deepnetts.tensor.TensorBase

Applies specified function to all elements in tensor in-place.

apply(DataSet<MLDataItem>) - Method in class deepnetts.data.norm.DecimalScaler

Performs normalization on the given inputs.

apply(DataSet<MLDataItem>) - Method in class deepnetts.data.norm.MaxScaler

Performs normalization on the given inputs.

apply(DataSet<MLDataItem>) - Method in class deepnetts.data.norm.MinMaxScaler

Performs normalization on the given inputs.

apply(DataSet<MLDataItem>) - Method in class deepnetts.data.norm.RangeScaler

Performs normalization on the given inputs.

apply(DataSet<MLDataItem>) - Method in class deepnetts.data.norm.Standardizer

apply(T) - Method in interface deepnetts.data.Preprocessing

applyWeightChanges() - Method in class deepnetts.net.layers.AbstractLayer

Applies weight changes to current weights Must be diferent for convolutional does nothing for MaxPooling Same for FullyConnected and OutputLayer

applyWeightChanges() - Method in class deepnetts.net.layers.ConvolutionalLayer

Apply weight changes calculated in backward pass

applyWeightChanges() - Method in class deepnetts.net.layers.FlattenLayer

applyWeightChanges() - Method in class deepnetts.net.layers.FullyConnectedLayer

applyWeightChanges() - Method in class deepnetts.net.layers.InputLayer

This method does nothing in input layer.

applyWeightChanges() - Method in class deepnetts.net.layers.MaxPoolingLayer

Does nothing for pooling layer since it does not have weights It just propagates deltas from next layer to previous through connections that had max activation in forward pass

applyWeightChanges() - Method in class deepnetts.net.layers.OutputLayer

Applies weight changes after one learning iteration or batch

applyWeightChanges() - Method in class deepnetts.net.NeuralNetwork

Applies calculated weight changes to all layers.

asJson() - Method in class deepnetts.net.train.TrainingResult

AugmentImagesTest - Class in deepnetts.util

just move 2(x) pix to left right up down

AugmentImagesTest() - Constructor for class deepnetts.util.AugmentImagesTest

average(ClassificationMetrics[]) - Static method in class deepnetts.eval.ClassificationMetrics

B

BackpropagationTrainer - Class in deepnetts.net.train: Backpropagation training algorithm for feed forward and convolutional neural networks.
BackpropagationTrainer(NeuralNetwork) - Constructor for class deepnetts.net.train.BackpropagationTrainer: Creates an instance of BackpropagationTrainer for the given neural network to train.
BackpropagationTrainer(Properties) - Constructor for class deepnetts.net.train.BackpropagationTrainer: Creates an instance of BackpropagationTrainer with the given properties.
backward() - Method in class deepnetts.cudnn.AbstractBackwardCuda: Performs forward pass.
backward() - Method in interface deepnetts.cudnn.BackwardAccelerator
backward() - Method in class deepnetts.cudnn.FullyConnectedBackwardCuda
backward() - Method in class deepnetts.cudnn.SoftMaxBackwardCuda
backward() - Method in class deepnetts.net.layers.AbstractLayer: This method should implement backward pass in subclasses
backward() - Method in class deepnetts.net.layers.ConvolutionalLayer: Backward pass for convolutional layer tweaks the weights in filters.
backward() - Method in class deepnetts.net.layers.FlattenLayer
backward() - Method in class deepnetts.net.layers.FullyConnectedLayer
backward() - Method in class deepnetts.net.layers.InputLayer: This method does nothing in the input layer, and should never be called.
backward() - Method in interface deepnetts.net.layers.Layer: Performs weight parameters adjustment in backward pass during training of a neural network.
backward() - Method in class deepnetts.net.layers.MaxPoolingLayer: backward pass for a max(x, y) operation has a simple interpretation as only routing the gradient to the input that had the highest value in the forward pass.
backward() - Method in class deepnetts.net.layers.OutputLayer: This method implements backward pass for the output layer.
backward() - Method in class deepnetts.net.layers.SoftmaxOutputLayer: Performs backward pass for this layer.
backward() - Method in class deepnetts.net.NeuralNetwork: Performs a backward bass across all layers in neural network, which is the calculation of corrections for the network internal parameters (weights).
BackwardAccelerator - Interface in deepnetts.cudnn
balanceDownsample(ImageSet, Map<String, Integer>) - Static method in class deepnetts.util.ImageSetUtils
balanceUpsample(ImageSet, Map<String, Integer>) - Static method in class deepnetts.util.ImageSetUtils: Balance specified data set by adding random images of each class, to get equal number of examples for each class.
BINARY - Enum constant in enum class deepnetts.util.ColumnType
BinaryCrossEntropyLoss - Class in deepnetts.net.loss: Cross Entropy Loss is a loss function used for binary classification tasks (two classes, single output which represents probability ).
BinaryCrossEntropyLoss(NeuralNetwork) - Constructor for class deepnetts.net.loss.BinaryCrossEntropyLoss
bins() - Method in class deepnetts.util.Statistics.Histogram
binWidth() - Method in class deepnetts.util.Statistics.Histogram
BoundingBox - Class in deepnetts.util: Koristi se verovatno u labeleru.
BoundingBox(int, int, int, int) - Constructor for class deepnetts.util.BoundingBox
BoundingBox(int, int, int, int, int) - Constructor for class deepnetts.util.BoundingBox
BoundingBox(int, int, int, int, int, float) - Constructor for class deepnetts.util.BoundingBox
BoundingBox(int, int, int, int, int, String, float) - Constructor for class deepnetts.util.BoundingBox
build() - Method in class deepnetts.net.ConvolutionalNetwork.Builder: Builds an instance of ConvolutionalNetwork with settings specified in this builder.
build() - Method in class deepnetts.net.FeedForwardNetwork.Builder: Builds an instance of FeedForwardNetwork with settings specified in this builder.
build() - Method in class deepnetts.net.train.KFoldCrossValidation.Builder
builder() - Static method in class deepnetts.net.ConvolutionalNetwork: Returns a builder for the ConvolutionalNetwork
builder() - Static method in class deepnetts.net.FeedForwardNetwork: Returns a builder for the FeedForwardNetwork
builder() - Static method in class deepnetts.net.train.KFoldCrossValidation
Builder() - Constructor for class deepnetts.net.ConvolutionalNetwork.Builder
Builder() - Constructor for class deepnetts.net.FeedForwardNetwork.Builder
Builder() - Constructor for class deepnetts.net.train.KFoldCrossValidation.Builder

C

c - Variable in class deepnetts.cudnn.CudaTensorLayout
calculate(float) - Method in class deepnetts.net.train.opt.AbstractOptimizer
calculateCellsPerThread(int, int) - Static method in class deepnetts.util.DeepNettsThreadPool
calculateDeltaBias(float, int) - Method in class deepnetts.net.train.opt.AdaDeltaOptimizer
calculateDeltaBias(float, int) - Method in class deepnetts.net.train.opt.AdaGradOptimizer
calculateDeltaBias(float, int) - Method in class deepnetts.net.train.opt.AdamOptimizer
calculateDeltaBias(float, int) - Method in class deepnetts.net.train.opt.MomentumOptimizer
calculateDeltaBias(float, int) - Method in interface deepnetts.net.train.opt.Optimizer
calculateDeltaBias(float, int) - Method in class deepnetts.net.train.opt.RmsPropOptimizer
calculateDeltaBias(float, int) - Method in class deepnetts.net.train.opt.SgdOptimizer
calculateDeltaWeight(float, int...) - Method in class deepnetts.net.train.opt.AdaDeltaOptimizer
calculateDeltaWeight(float, int...) - Method in class deepnetts.net.train.opt.AdaGradOptimizer
calculateDeltaWeight(float, int...) - Method in class deepnetts.net.train.opt.AdamOptimizer
calculateDeltaWeight(float, int...) - Method in class deepnetts.net.train.opt.MomentumOptimizer
calculateDeltaWeight(float, int...) - Method in interface deepnetts.net.train.opt.Optimizer
calculateDeltaWeight(float, int...) - Method in class deepnetts.net.train.opt.RmsPropOptimizer
calculateDeltaWeight(float, int...) - Method in class deepnetts.net.train.opt.SgdOptimizer
call() - Method in class deepnetts.util.CallableConvolutionalConsumerWithBuffers
call() - Method in class deepnetts.util.CallableIntConsumer
call() - Method in class deepnetts.util.CallableRangeConsumer
CallableConvolutionalConsumerWithBuffers - Class in deepnetts.util: Callable int consumer.
CallableConvolutionalConsumerWithBuffers(int, int, float[], float[], ConvolutionalChannelConsumer) - Constructor for class deepnetts.util.CallableConvolutionalConsumerWithBuffers
CallableIntConsumer - Class in deepnetts.util: Callable int consumer.
CallableIntConsumer(int, int, IntConsumer) - Constructor for class deepnetts.util.CallableIntConsumer
CallableRangeConsumer - Class in deepnetts.util: Callable range consumer.
CallableRangeConsumer(int, int, RangeConsumer) - Constructor for class deepnetts.util.CallableRangeConsumer
CENTER - Enum constant in enum class deepnetts.util.ImageResize
CenterOnWhiteBackground - Class in deepnetts.util: Center images on backgounds and save at target path.
CenterOnWhiteBackground() - Constructor for class deepnetts.util.CenterOnWhiteBackground
channelMean(int) - Method in class deepnetts.tensor.Tensor3D
checkLicense() - Static method in class deepnetts.core.DeepNetts: Validates the Deep Netts license.
checkLicense() - Method in class deepnetts.util.LicenseChecker
cIdx - Variable in class deepnetts.tensor.Layout
ClassificationMetrics - Class in deepnetts.eval: Various metrics that tell us how good is a classifier.
ClassificationMetrics(int, int, int, int) - Constructor for class deepnetts.eval.ClassificationMetrics: Constructs a new classification metrics using specified arguments.
ClassificationMetrics(ConfusionMatrix) - Constructor for class deepnetts.eval.ClassificationMetrics: Constructs a new classification metrics from specified confusion matrix.
ClassificationMetrics(ConfusionMatrix, String, int) - Constructor for class deepnetts.eval.ClassificationMetrics: Constructs a new classification metrics of a single class for multi class classification.
ClassificationMetrics.Stats - Class in deepnetts.eval: Average values of commonly used classification metrics.
ClassifierEvaluator - Class in deepnetts.eval: Evaluation method for binary and multi-class classifiers.
ClassifierEvaluator() - Constructor for class deepnetts.eval.ClassifierEvaluator
clone() - Method in class deepnetts.tensor.Shape
clone() - Method in class deepnetts.tensor.Tensor1D
clone() - Method in class deepnetts.tensor.Tensor2D
clone() - Method in class deepnetts.tensor.TensorBase
COL_IDX - Static variable in interface deepnetts.net.train.opt.Optimizer
COL_IDX - Static variable in class deepnetts.tensor.Shape
ColorUtils - Class in deepnetts.util
cols() - Method in class deepnetts.tensor.Tensor2D
cols() - Method in class deepnetts.tensor.Tensor3D
cols() - Method in class deepnetts.tensor.Tensor4D
ColumnType - Enum Class in deepnetts.util
COMPANY - Static variable in class deepnetts.util.LicenseChecker
ConfusionMatrix - Class in deepnetts.eval: Confusion matrix contains raw classifier test results.
ConfusionMatrix(String[]) - Constructor for class deepnetts.eval.ConfusionMatrix: Creates a new confusion matrix for specified class labels
CONVOLUTIONAL - Enum constant in enum class deepnetts.net.layers.LayerType: Performs detection of pixel patterns.
CONVOLUTIONAL - Enum constant in enum class deepnetts.net.NetworkType
ConvolutionalChannelConsumer - Interface in deepnetts.util
ConvolutionalForwardCuda - Class in deepnetts.cudnn
ConvolutionalForwardCuda(CudaHandles, ConvolutionalLayer) - Constructor for class deepnetts.cudnn.ConvolutionalForwardCuda
ConvolutionalLayer - Class in deepnetts.net.layers: Convolutional layer performs image convolution operation on outputs of a previous layer using filters.
ConvolutionalLayer(int, int, int) - Constructor for class deepnetts.net.layers.ConvolutionalLayer: Create a new instance of convolutional layer with specified number of channels filter size, default padding (filter-1)/2, and default stride stride value 1, and specified number of channels.
ConvolutionalLayer(int, int, int, int, ActivationType) - Constructor for class deepnetts.net.layers.ConvolutionalLayer
ConvolutionalLayer(int, Filter) - Constructor for class deepnetts.net.layers.ConvolutionalLayer
ConvolutionalLayer(int, Filter, ActivationType) - Constructor for class deepnetts.net.layers.ConvolutionalLayer
ConvolutionalNetwork - Class in deepnetts.net: Convolutional neural network is an extension of feed forward network, which can include 2D and 3D adaptive preprocessing layers (Convolutional and MaxPooling layer), which is specialized to learn to recognize features in images.
ConvolutionalNetwork.Builder - Class in deepnetts.net: Builder for a convolutional neural network.
copy() - Method in class deepnetts.tensor.Tensor1D
copy() - Method in class deepnetts.tensor.Tensor2D
copy() - Method in class deepnetts.tensor.Tensor3D
copy() - Method in class deepnetts.tensor.Tensor4D
copy() - Method in class deepnetts.tensor.TensorBase
copy(float[], float[]) - Static method in class deepnetts.tensor.Tensors
copy(TensorBase, TensorBase) - Static method in class deepnetts.tensor.Tensors
copy2DSubChannel(int, float[]) - Method in class deepnetts.tensor.Tensor3D: Copies 2D channel values from this tensor into given float array buffer.
copy3DSubChannel(int, float[]) - Method in class deepnetts.tensor.Tensor4D
copyFrom(float[]) - Method in class deepnetts.tensor.TensorBase
copyFrom(TensorBase) - Method in class deepnetts.tensor.TensorBase
copyFromGPU() - Method in class deepnetts.tensor.TensorBase
copyOf(float[]) - Static method in class deepnetts.tensor.Tensors
copyTensorsToDevice() - Method in class deepnetts.cudnn.AbstractBackwardCuda
copyTensorsToDevice() - Method in class deepnetts.cudnn.AbstractForwardCuda
copyTensorsToDevice() - Method in class deepnetts.cudnn.ConvolutionalForwardCuda
copyToDev() - Method in class deepnetts.cudnn.CudaTensorBridge
copyToDev() - Method in interface deepnetts.util.TensorAcceleratorBridge
copyToGPU() - Method in class deepnetts.tensor.TensorBase
copyToHost() - Method in class deepnetts.cudnn.CudaTensorBridge
copyToHost() - Method in interface deepnetts.util.TensorAcceleratorBridge
correlation(float[], float[]) - Static method in class deepnetts.util.Statistics: Calculates and returns Pearson correlation coefficient for the given arrays.
correlationCoefficient - Variable in class deepnetts.eval.ClassificationMetrics.Stats
correlationMatrix(float[][]) - Static method in class deepnetts.util.Statistics
count() - Method in class deepnetts.util.Stats
countByClasses() - Method in class deepnetts.data.ImageSet
countMissingValues() - Method in class deepnetts.data.TabularDataSet
countMissingValues(int) - Method in class deepnetts.data.TabularDataSet
create(int, int, float[]) - Static method in class deepnetts.tensor.Tensors: Factory method for creating tensor instance,
create(int, int, int, float[]) - Static method in class deepnetts.tensor.Tensors
create(int, int, int, int, float[]) - Static method in class deepnetts.tensor.Tensors
create(ActivationType) - Static method in interface deepnetts.net.layers.activation.ActivationFunction: Creates and returns specified type of activation function.
create(OptimizerType, AbstractLayer) - Static method in interface deepnetts.net.train.opt.Optimizer: Factory method to create different types of optimizers
createAcceleratorBridge() - Method in class deepnetts.tensor.TensorBase
createBatchedDataset(TabularDataSet<?>, int) - Static method in class deepnetts.data.DataSets
createColsCache() - Method in class deepnetts.tensor.Tensor2D
createConvolutionalNetworkFromJson(JSONObject) - Static method in class deepnetts.util.FileIO
createDataSetFromRawImages(String, String, int, int, boolean) - Static method in class deepnetts.util.ImageSetUtils: Creates image data set from raw images by resizing and randomly croping to target dimensions.
createFeedForwardNetworkFromJson(JSONObject) - Static method in class deepnetts.util.FileIO
createFilterCache() - Method in class deepnetts.net.layers.ConvolutionalLayer
createFrom(ConfusionMatrix) - Static method in class deepnetts.eval.ClassificationMetrics: Creates classification metrics from the given confusion matrix.
createFromFile(File) - Static method in class deepnetts.util.FileIO
createFromFile(File, Class<T>) - Static method in class deepnetts.util.FileIO
createFromFile(String, Class<T>) - Static method in class deepnetts.util.FileIO
createFromJson(File) - Static method in class deepnetts.util.FileIO
createFromJson(String) - Static method in class deepnetts.util.FileIO
createFromJson(JSONObject) - Static method in class deepnetts.util.FileIO
createImageIndex(String) - Static method in class deepnetts.util.ImageSetUtils: Creates an index file with list of all files in all subdirectories and write them into single index.txt file.
CreateImageIndex - Class in deepnetts.util
CreateImageIndex() - Constructor for class deepnetts.util.CreateImageIndex
createIndexFile(HashMap<File, BufferedImage>, String, boolean) - Static method in class deepnetts.util.ImageUtils
createLabelsIndex(String) - Static method in class deepnetts.util.ImageSetUtils: Creates a file with list of labels/categories from subdirectories at the specified given path.
CreateLabelsIndex - Class in deepnetts.util
CreateLabelsIndex() - Constructor for class deepnetts.util.CreateLabelsIndex
createNeuralNetwork(Properties) - Method in class deepnetts.automl.FeedForwardNetworkFactory
createNeuralNetwork(Properties) - Method in interface deepnetts.automl.NetworkFactory: Creates and returns a neural network of specified type
createRandomlyCroppedImages(String, String, int, int, int) - Static method in class deepnetts.util.ImageSetUtils
createRowsAndColsCache() - Method in class deepnetts.tensor.Tensor2D
createRowsCache() - Method in class deepnetts.tensor.Tensor2D
createSearchSpace() - Method in class deepnetts.automl.Parameters: Returns parameters search space - all possible combination of all given parameter values.
createsTrainingSnaphots() - Method in class deepnetts.net.train.BackpropagationTrainer: Returns true if network creates training snapshots, false otherwise.
createSubSampledImageIndex(String, String, int, boolean) - Static method in class deepnetts.util.ImageSetUtils: Copies specified number of samples of each class from
cropAtCornersAndCenter(BufferedImage, int, int) - Static method in class deepnetts.util.ImageUtils: Returns five crops of a given image: four from each corner and one at center
CROSS_ENTROPY - Enum constant in enum class deepnetts.net.loss.LossType: Cross Entropy Loss, used for classificaton tasks, implemented by CrossEntropyLoss
CrossEntropyLoss - Class in deepnetts.net.loss: Average Cross Entropy Loss function commonly used for multi class classification problems.
CrossEntropyLoss(NeuralNetwork) - Constructor for class deepnetts.net.loss.CrossEntropyLoss
CsvFormat - Class in deepnetts.util: CSV file format options: delimiter, header(column names) and column types.
CsvFormat() - Constructor for class deepnetts.util.CsvFormat
CudaHandles - Class in deepnetts.cudnn: Singleton for native CUDA handles
CudaHandles() - Constructor for class deepnetts.cudnn.CudaHandles
CudaTensor - Class in deepnetts.cudnn
CudaTensor(TensorBase) - Constructor for class deepnetts.cudnn.CudaTensor
CudaTensorBridge - Class in deepnetts.cudnn: Bridge to tensor on cuda/gpu device.
CudaTensorBridge(Tensor2D, boolean) - Constructor for class deepnetts.cudnn.CudaTensorBridge
CudaTensorBridge(TensorBase) - Constructor for class deepnetts.cudnn.CudaTensorBridge: Alocate tensor on cuda device, copy from jvm memory to cuda and back
CudaTensorLayout - Class in deepnetts.cudnn: CUDA Tensor layout
CudaTensorLayout(int, int, int, int) - Constructor for class deepnetts.cudnn.CudaTensorLayout
CudaUtils - Class in deepnetts.cudnn
CudaUtils() - Constructor for class deepnetts.cudnn.CudaUtils

D

DataSets - Class in deepnetts.data: Data set utility methods ans constants.
DataSets() - Constructor for class deepnetts.data.DataSets
DEBUG - Enum constant in enum class deepnetts.net.Mode
DECIMAL - Enum constant in enum class deepnetts.util.ColumnType
DecimalScaler - Class in deepnetts.data.norm: Decimal scale normalization for the given data set.
DecimalScaler(DataSet<MLDataItem>) - Constructor for class deepnetts.data.norm.DecimalScaler: Creates a new instance of max normalizer initialized to max values in given data set.
DeepNetts - Class in deepnetts.core: Global configuration and settings for Deep Netts Engine.
deepnetts.automl - package deepnetts.automl: Support for automatically building deep learning models using hyper-parameter search.
deepnetts.core - package deepnetts.core: Core engine configuration and settings and runtime properties.
deepnetts.cudnn - package deepnetts.cudnn
deepnetts.data - package deepnetts.data: Data structures to store example data used for building machine learning models.
deepnetts.data.norm - package deepnetts.data.norm: Data normalization methods, used to scale data to specific range, in order to make them suitable for use by a neural network.
deepnetts.eval - package deepnetts.eval: Evaluation procedures for machine learning models, used to estimate how good models are performing when given new data that (that was not used for training).
deepnetts.net - package deepnetts.net: Neural network architectures with their corresponding builders.
deepnetts.net.layers - package deepnetts.net.layers: Neural network layers, which are main building blocks of a neural network.
deepnetts.net.layers.activation - package deepnetts.net.layers.activation: Activation functions for neural network layers.
deepnetts.net.loss - package deepnetts.net.loss: Commonly used loss functions, which are used to calculate error during the training as a difference between predicted and target output.
deepnetts.net.train - package deepnetts.net.train: Training algorithms and related utilities.
deepnetts.net.train.opt - package deepnetts.net.train.opt: Optimization methods used by training algorithm.
deepnetts.net.weights - package deepnetts.net.weights: Weights randomization techniques, used for initializing layer's internal parameters.
deepnetts.tensor - package deepnetts.tensor
deepnetts.util - package deepnetts.util: Various utility classes including Tensor, image operations, multithreading, exceptions etc.
DeepNettsException - Exception Class in deepnetts.util
DeepNettsException() - Constructor for exception class deepnetts.util.DeepNettsException: Creates a new instance of JDeepNettsException without detail message.
DeepNettsException(String) - Constructor for exception class deepnetts.util.DeepNettsException: Constructs an instance of JDeepNettsException with the specified detail message.
DeepNettsException(String, Throwable) - Constructor for exception class deepnetts.util.DeepNettsException
DeepNettsException(Throwable) - Constructor for exception class deepnetts.util.DeepNettsException
DeepNettsLogManager - Class in deepnetts.util
DeepNettsLogManager() - Constructor for class deepnetts.util.DeepNettsLogManager
DeepNettsRunner - Class in deepnetts.util
DeepNettsRunner() - Constructor for class deepnetts.util.DeepNettsRunner
DeepNettsThreadPool - Class in deepnetts.util: Dedicated thread pool for Deep Netts Engine.
DeepNettsThreadPool() - Constructor for class deepnetts.util.DeepNettsThreadPool
DeepNettsThreadPool(int) - Constructor for class deepnetts.util.DeepNettsThreadPool
DeleteAugmentImages - Class in deepnetts.util: just move 2(x) pix to left right up down
DeleteAugmentImages() - Constructor for class deepnetts.util.DeleteAugmentImages
DELIMITER_COMMA - Static variable in class deepnetts.data.DataSets
DELIMITER_SEMICOLON - Static variable in class deepnetts.data.DataSets
DELIMITER_SPACE - Static variable in class deepnetts.data.DataSets
DELIMITER_TAB - Static variable in class deepnetts.data.DataSets
deNormalizeInputs(TensorBase) - Method in class deepnetts.data.norm.MaxScaler
deNormalizeOutputs(TensorBase) - Method in class deepnetts.data.norm.MaxScaler: De-normalize given output vector in-place.
depth() - Method in class deepnetts.tensor.Tensor3D
depth() - Method in class deepnetts.tensor.Tensor4D
DEPTH_IDX - Static variable in interface deepnetts.net.train.opt.Optimizer
DEPTH_IDX - Static variable in class deepnetts.tensor.Shape
descriptiveStatistics(float[]) - Static method in class deepnetts.util.Statistics
destroyHandles() - Method in class deepnetts.cudnn.CudaHandles
detectCsvFormat(String) - Static method in class deepnetts.data.DataSets
DEVICE_ID - Static variable in class deepnetts.util.LicenseChecker
div(float) - Method in class deepnetts.tensor.TensorBase: Divide all values in this tensor with specified value.
div(float[]) - Method in class deepnetts.tensor.TensorBase
div(float[], float) - Static method in class deepnetts.tensor.Tensors
div(float[], float[]) - Static method in class deepnetts.tensor.Tensors
div(TensorBase) - Method in class deepnetts.tensor.TensorBase: Element-wise divison with specified tensor.
divChannel(float, int) - Method in class deepnetts.tensor.Tensor3D
DIVIDE_AND_CONQUER - Static variable in class deepnetts.automl.HyperParameterSearch
dotProd(Tensor2D, Tensor2D) - Method in class deepnetts.tensor.Tensor2D: Performs dot product operation on this tensor and matrib, and stores results in result tensor.
dotProdFromCache(Tensor1D, Tensor1D) - Method in class deepnetts.tensor.Tensor2D: Performs dot product operation on this 2d tensor (matrix) and vector , and stores results in result tensor.
dotProduct(Tensor2D, Tensor1D, Tensor1D) - Static method in class deepnetts.tensor.Tensors: Dot product matrix vector multiplication C = A .
dotProduct(Tensor2D, Tensor2D, Tensor2D) - Static method in class deepnetts.tensor.Tensors
dotProductBuffered(Tensor2D, Tensor1D, Tensor1D) - Static method in class deepnetts.tensor.Tensors: Matrix vector dot product.
dotProductBuffered(Tensor2D, Tensor2D, Tensor2D) - Static method in class deepnetts.tensor.Tensors

E

EMAIL - Static variable in class deepnetts.util.LicenseChecker
ENUM - Enum constant in enum class deepnetts.util.ColumnType
EPOCH_FINISHED - Enum constant in enum class deepnetts.net.train.TrainingEvent.Type: Specifies a training epoch finished event
EPOCH_FINISHED - Static variable in class deepnetts.net.train.TrainingEvent: Specifies that training epoch has finished.
EPOCH_STARTED - Enum constant in enum class deepnetts.net.train.TrainingEvent.Type: Specifies a training epoch started event
EPOCH_STARTED - Static variable in class deepnetts.net.train.TrainingEvent: Specifies that training epoch has started.
EPS - Static variable in interface deepnetts.net.train.opt.Optimizer: Smoothing term to prevent division by zero if sqr grad sum becomes zero 1e-8 should be also tried https://d2l.ai/chapter_optimization/adagrad.html 1e-6 The value to use is 1e-6, 1e-8, Keras uses 1e-7 for adam
equals(TensorBase, float) - Method in class deepnetts.tensor.TensorBase
equals(Object) - Method in class deepnetts.automl.Parameter
equals(Object) - Method in class deepnetts.automl.Parameters.ParameterCombination
equals(Object) - Method in class deepnetts.tensor.Shape
equals(Object) - Method in class deepnetts.tensor.TensorBase
equalsName(String) - Method in enum class deepnetts.net.layers.LayerType
equalsName(String) - Method in enum class deepnetts.net.loss.LossType
equalsName(String) - Method in enum class deepnetts.net.NetworkType
errorCheck(int, String) - Static method in class deepnetts.cudnn.CudaUtils
evaluate(NeuralNetwork, DataSet<? extends MLDataItem>) - Method in class deepnetts.eval.ClassifierEvaluator: Performs classifier evaluation and returns classification performance metrics.
evaluate(NeuralNetwork, DataSet<? extends MLDataItem>) - Method in class deepnetts.eval.RegresionEvaluator
evaluateClassifier(NeuralNetwork<?>, DataSet<? extends MLDataItem>) - Static method in class deepnetts.eval.Evaluators
evaluateRegressor(NeuralNetwork<?>, DataSet<? extends MLDataItem>) - Static method in class deepnetts.eval.Evaluators: Evaluates specified neural network with test set, as a regression model and returns basic regression evauation metrics.
evaluator(Evaluator<NeuralNetwork, DataSet<? extends MLDataItem>>) - Method in class deepnetts.automl.HyperParameterSearch
evaluator(Evaluator<NeuralNetwork, DataSet<? extends MLDataItem>>) - Method in class deepnetts.net.train.KFoldCrossValidation.Builder
Evaluators - Class in deepnetts.eval: Utility methods for evaluating machine learning models.
ExampleImage - Class in deepnetts.data: Example image to train a deep learning model.
ExampleImage(BufferedImage) - Constructor for class deepnetts.data.ExampleImage
ExampleImage(BufferedImage, String) - Constructor for class deepnetts.data.ExampleImage
ExampleImage(BufferedImage, String, int) - Constructor for class deepnetts.data.ExampleImage
ExampleImage(BufferedImage, String, int, int) - Constructor for class deepnetts.data.ExampleImage
ExampleImage(File, BufferedImage, String) - Constructor for class deepnetts.data.ExampleImage
ExampleImage(File, String) - Constructor for class deepnetts.data.ExampleImage: Creates an instance of new example image with specified image and label Loads image from specified file and creates matrix structures with color information
ExampleImage(String, String) - Constructor for class deepnetts.data.ExampleImage

F

FALSE_NEGATIVE - Static variable in class deepnetts.eval.ConfusionMatrix: A label for items falsely classified as negative, which are actually positive.
FALSE_POSITIVE - Static variable in class deepnetts.eval.ConfusionMatrix: A label for items falsely classified as positive, which are actually negative.
FEEDFORWARD - Enum constant in enum class deepnetts.net.NetworkType
FeedForwardNetwork - Class in deepnetts.net: Feed forward neural network architecture, also known as Multi Layer Perceptron.
FeedForwardNetwork.Builder - Class in deepnetts.net: Builder of a FeedForwardNetwork instance.
FeedForwardNetworkFactory - Class in deepnetts.automl: Factory for FeedForwardNetwork.
FeedForwardNetworkFactory() - Constructor for class deepnetts.automl.FeedForwardNetworkFactory
FILE_TRAINING_PROP - Static variable in class deepnetts.util.DeepNettsRunner
FileIO - Class in deepnetts.util: File utilities for saving and loading neural networks.
fill(float) - Method in class deepnetts.tensor.TensorBase: Fills the entire tensor with specified value.
fillFourthDim(Tensor4D, int, float) - Static method in class deepnetts.tensor.Tensors
Filter - Class in deepnetts.net.layers: Settings of a convolutional filter which is used to learn to detect pixel patterns.
Filter(int) - Constructor for class deepnetts.net.layers.Filter
Filter(int, int) - Constructor for class deepnetts.net.layers.Filter
Filters - Class in deepnetts.net.layers
Filters() - Constructor for class deepnetts.net.layers.Filters
FlattenForwardCuda - Class in deepnetts.cudnn
FlattenForwardCuda(CudaHandles, AbstractLayer) - Constructor for class deepnetts.cudnn.FlattenForwardCuda
FlattenLayer - Class in deepnetts.net.layers: Transforms outputs from previous 3D layer into a flatten 1D tensor in forward pass, Backward pass propagates weighted errors/deltas from the next fully connected layer.
FlattenLayer() - Constructor for class deepnetts.net.layers.FlattenLayer
flipHorizontal(BufferedImage) - Static method in class deepnetts.util.ImageUtils
flipVertical(BufferedImage) - Static method in class deepnetts.util.ImageUtils
forward() - Method in class deepnetts.cudnn.AbstractForwardCuda: Performs forward pass.
forward() - Method in class deepnetts.cudnn.ConvolutionalForwardCuda
forward() - Method in class deepnetts.cudnn.FlattenForwardCuda
forward() - Method in interface deepnetts.cudnn.ForwardAccelerator
forward() - Method in class deepnetts.cudnn.FullyConnectedForwardCuda
forward() - Method in class deepnetts.cudnn.MaxPoolingForwardCuda
forward() - Method in class deepnetts.cudnn.SoftMaxForwardCuda
forward() - Method in class deepnetts.net.layers.AbstractLayer: This method should implement forward pass in subclasses
forward() - Method in class deepnetts.net.layers.ConvolutionalLayer: Forward pass for convolutional layer.
forward() - Method in class deepnetts.net.layers.FlattenLayer
forward() - Method in class deepnetts.net.layers.FullyConnectedLayer
forward() - Method in class deepnetts.net.layers.InputLayer: This method does nothing in the input layer, and should never be called.
forward() - Method in interface deepnetts.net.layers.Layer: Performs layer calculation in forward pass of a neural network.
forward() - Method in class deepnetts.net.layers.MaxPoolingLayer: Max pooling forward pass outputs the max value for each filter position.
forward() - Method in class deepnetts.net.layers.OutputLayer: This method implements forward pass for the output layer.
forward() - Method in class deepnetts.net.layers.SoftmaxOutputLayer: This method implements forward pass for the output layer.
ForwardAccelerator - Interface in deepnetts.cudnn
FOURTH_DIM_IDX - Static variable in interface deepnetts.net.train.opt.Optimizer
fourthDim() - Method in class deepnetts.tensor.Tensor4D
freeOnDevice() - Method in class deepnetts.cudnn.CudaTensorBridge
fromFile(String) - Static method in class deepnetts.tensor.Tensor2D
fromFile(String) - Static method in class deepnetts.tensor.Tensor3D
fromFile(String) - Static method in class deepnetts.tensor.Tensors
fScore - Variable in class deepnetts.eval.ClassificationMetrics.Stats
FULLY_CONNECTED - Enum constant in enum class deepnetts.net.layers.LayerType: Looks for patterns in inputs.
FullyConnectedBackwardCuda - Class in deepnetts.cudnn
FullyConnectedBackwardCuda(CudaHandles, FullyConnectedLayer) - Constructor for class deepnetts.cudnn.FullyConnectedBackwardCuda
FullyConnectedBackwardCuda(CudaHandles, TensorBase, TensorBase, TensorBase, TensorBase) - Constructor for class deepnetts.cudnn.FullyConnectedBackwardCuda
FullyConnectedForwardCuda - Class in deepnetts.cudnn
FullyConnectedForwardCuda(CudaHandles, FullyConnectedLayer) - Constructor for class deepnetts.cudnn.FullyConnectedForwardCuda
FullyConnectedForwardCuda(CudaHandles, TensorBase, TensorBase, TensorBase, TensorBase) - Constructor for class deepnetts.cudnn.FullyConnectedForwardCuda
FullyConnectedLayer - Class in deepnetts.net.layers: Fully connected layer is used as a hidden layer in a neural network, and it has a single row of units/nodes/neurons connected to all neurons in previous and next layer.
FullyConnectedLayer(int) - Constructor for class deepnetts.net.layers.FullyConnectedLayer: Creates an instance of fully connected layer with specified number of neurons and ReLU activation function.
FullyConnectedLayer(int, ActivationType) - Constructor for class deepnetts.net.layers.FullyConnectedLayer: Creates an instance of fully connected layer with specified width (number of neurons) and activation function type.

G

gaussian(float[], float, float) - Static method in class deepnetts.net.weights.RandomWeights
GAUSSIAN - Enum constant in enum class deepnetts.net.weights.RandomWeightsType
generateNoisyImage(int, int, int, String) - Static method in class deepnetts.util.ImageUtils
generateRandomColoredImages(int, int, int, String) - Static method in class deepnetts.util.ImageUtils: Generates a specified number of randomly full colored images of a specified size.
generateRandomFilledRectImages(int, int, int, String) - Static method in class deepnetts.util.ImageUtils
GenerateRandomNegative - Class in deepnetts.util
GenerateRandomNegative() - Constructor for class deepnetts.util.GenerateRandomNegative
get(int) - Method in class deepnetts.tensor.Tensor1D: Gets value from specified index position.
get(int, int) - Method in class deepnetts.eval.ConfusionMatrix: Returns a value of confusion matrix at specified position.
get(int, int) - Method in class deepnetts.tensor.Tensor2D: Returns matrix value at row, col
get(int, int, int) - Method in class deepnetts.tensor.Tensor3D
get(int, int, int, int) - Method in class deepnetts.tensor.Tensor4D
get(String) - Method in class deepnetts.automl.Parameters
get(String) - Method in class deepnetts.util.Parameters
getAcceleratorBridge() - Method in class deepnetts.tensor.TensorBase
getAccuracy() - Method in class deepnetts.eval.ClassificationMetrics: Percent of correct classifications (for both positive and negative classes).
getActivation() - Method in class deepnetts.net.layers.AbstractLayer
getActivationType() - Method in class deepnetts.net.layers.AbstractLayer
getAsProperties() - Method in class deepnetts.automl.Parameters.ParameterCombination
getBalancedAccuracy() - Method in class deepnetts.eval.ClassificationMetrics: Balanced accuracy is a good metric to use when data set is not balanced.
getBatchSize() - Method in class deepnetts.net.layers.AbstractLayer
getBatchSize() - Method in class deepnetts.net.train.BackpropagationTrainer: Batch size is number of training examples after which network's weights are adjusted.
getBestNetwork() - Method in class deepnetts.net.train.KFoldCrossValidation
getBestResult() - Method in class deepnetts.net.train.KFoldCrossValidation
getBiases() - Method in class deepnetts.net.layers.AbstractLayer
getBlue(int) - Static method in class deepnetts.util.ColorUtils
getBoolean(String) - Method in class deepnetts.util.Parameters
getBoolean(String) - Method in class deepnetts.util.TypedProperties: Get specified property as boolean.
getBrightness() - Method in class deepnetts.data.ImageSet
getCheckpointEpochs() - Method in class deepnetts.net.train.BackpropagationTrainer: On how many epochs the snapshots of the trained network should be created.
getClassCount() - Method in class deepnetts.eval.ConfusionMatrix
getClassIdx() - Method in class deepnetts.eval.ClassificationMetrics
getClassLabel() - Method in class deepnetts.eval.ClassificationMetrics: Returns class label that these metric correspond to (used for multi class classification).
getClassLabels() - Method in class deepnetts.eval.ConfusionMatrix
getCol(int, float[]) - Method in class deepnetts.tensor.Tensor2D
getColorFor(float, float, float) - Static method in class deepnetts.util.ColorUtils
getColsCache(int) - Method in class deepnetts.tensor.Tensor2D
getColumnNames() - Method in class deepnetts.data.TabularDataSet
getColumnNames() - Method in class deepnetts.util.CsvFormat
getColumnTypes() - Method in class deepnetts.util.CsvFormat
getConfusionMatrix() - Method in class deepnetts.eval.ClassificationMetrics: Returns a confusion matrix that is used to generate these metrics.
getConfusionMatrix() - Method in class deepnetts.eval.ClassifierEvaluator
getCropCornersAndCenter() - Method in class deepnetts.data.ImageSet
getCublasHandle() - Method in class deepnetts.cudnn.CudaHandles
getCudaBridge() - Method in class deepnetts.cudnn.CudaTensor
getCudnnActivation(ActivationType) - Static method in class deepnetts.cudnn.CudaUtils
getCudnnHandle() - Method in class deepnetts.cudnn.CudaHandles
getCurrentEpoch() - Method in class deepnetts.net.train.BackpropagationTrainer: Returns the current training epoch(iteration) of this trainer.
getDefault() - Static method in class deepnetts.util.RandomGenerator: Returns the default global instance of random generator
getDelimiter() - Method in class deepnetts.data.ImageSet
getDelimiter() - Method in class deepnetts.util.CsvFormat
getDeltaBiases() - Method in class deepnetts.net.layers.AbstractLayer
getDeltas() - Method in class deepnetts.net.layers.AbstractLayer
getDeltas() - Method in interface deepnetts.net.layers.Layer: Returns layer deltas/errors (as a tensor).
getDeltaWeights() - Method in class deepnetts.net.ConvolutionalNetwork: Returns delta weights for all layers.
getDeltaWeights() - Method in class deepnetts.net.layers.AbstractLayer
getDepth() - Method in class deepnetts.net.layers.AbstractLayer
getDim(int) - Method in class deepnetts.tensor.Shape
getDimensions() - Method in class deepnetts.tensor.Shape
getDouble(String) - Method in class deepnetts.util.TypedProperties: Get specified property as double .
getDoubleArray(String) - Method in class deepnetts.util.TypedProperties
getDropout() - Method in class deepnetts.net.layers.FullyConnectedLayer
getDropout() - Method in class deepnetts.net.train.BackpropagationTrainer: Dropout is a technique to prevent overfitting, which skips adjusting weights for some neurons with given probability.
getEarlyStopping() - Method in class deepnetts.net.train.BackpropagationTrainer: Early stopping stops training if it starts converging slow, and prevents overfitting.
getEarlyStoppingMinLossChange() - Method in class deepnetts.net.train.BackpropagationTrainer: Early stopping stops training if the error/loss start converging to slow.
getEarlyStoppingPatience() - Method in class deepnetts.net.train.BackpropagationTrainer: How many epochs to wait to see if the loss is lowering to slow.
getEpochs() - Method in class deepnetts.net.train.TrainingResult
getError() - Method in class deepnetts.data.ExampleImage
getError() - Method in interface deepnetts.data.MLDataItem: Returns a model output error for this data item.
getError() - Method in class deepnetts.data.TabularDataSet.Item
getErrorRate() - Method in class deepnetts.eval.ClassificationMetrics: A percent of wrong classifications/predictions made.
getEvaluationMetrics() - Method in class deepnetts.net.train.TrainingResult
getEvaluator() - Method in class deepnetts.automl.HyperParameterSearch
getExtendedLogging() - Method in class deepnetts.net.train.BackpropagationTrainer: Extended logging includes additional info for debugging the training.
getF1Score() - Method in class deepnetts.eval.ClassificationMetrics: Calculates and returns F1 score - a balance between recall and precision.
getFalseDiscoveryRate() - Method in class deepnetts.eval.ClassificationMetrics: When its actually no, how often it is classified as yes
getFalseNegative() - Method in class deepnetts.eval.ConfusionMatrix: How many positive items has been (falsely) classified as negative.
getFalseNegative(int) - Method in class deepnetts.eval.ConfusionMatrix
getFalseNegativeRate() - Method in class deepnetts.eval.ClassificationMetrics: When its actually yes, how often does it predicts no
getFalsePositive() - Method in class deepnetts.eval.ConfusionMatrix: Returns number of false positive classifications.
getFalsePositive(int) - Method in class deepnetts.eval.ConfusionMatrix
getFalsePositiveRate() - Method in class deepnetts.eval.ClassificationMetrics: When it's actually no, how often does it predict yes? FP/actual no
getFile() - Method in class deepnetts.data.ExampleImage
getFileHandler() - Method in class deepnetts.util.DeepNettsLogManager
getFilterDeltaWeights() - Method in class deepnetts.net.layers.ConvolutionalLayer
getFilterDepth() - Method in class deepnetts.net.layers.ConvolutionalLayer
getFilterHeight() - Method in class deepnetts.net.layers.ConvolutionalLayer
getFilterHeight() - Method in class deepnetts.net.layers.MaxPoolingLayer
getFilters() - Method in class deepnetts.net.layers.ConvolutionalLayer
getFilterWidth() - Method in class deepnetts.net.layers.ConvolutionalLayer
getFilterWidth() - Method in class deepnetts.net.layers.MaxPoolingLayer
getFlipHorizontal() - Method in class deepnetts.data.ImageSet
getFloat(String) - Method in class deepnetts.util.Parameters
getFloat(String) - Method in class deepnetts.util.TypedProperties: Get specified property as float.
getFloatArray(String) - Method in class deepnetts.util.TypedProperties
getForwardAccelerator() - Method in class deepnetts.net.layers.AbstractLayer
getFScore(int) - Method in class deepnetts.eval.ClassificationMetrics: Balance between precision and recall.
getFStat() - Method in class deepnetts.eval.RegressionMetrics
getGradients() - Method in class deepnetts.net.layers.AbstractLayer
getGrayscale() - Method in class deepnetts.data.ImageSet
getGreen(int) - Static method in class deepnetts.util.ColorUtils
getGroups() - Method in class deepnetts.net.layers.Filter
getHeight() - Method in class deepnetts.data.ExampleImage
getHeight() - Method in class deepnetts.net.layers.AbstractLayer
getHeight() - Method in class deepnetts.net.layers.Filter
getHeight() - Method in class deepnetts.util.BoundingBox
getHostPointer() - Method in class deepnetts.cudnn.CudaTensorBridge
getId() - Method in class deepnetts.util.BoundingBox
getImageHeight() - Method in class deepnetts.data.ImageSet
getImageType(File) - Static method in class deepnetts.util.ImageUtils: Returns the extension of the given image file.
getImageWidth() - Method in class deepnetts.data.ImageSet
getInput() - Method in class deepnetts.data.ExampleImage
getInput() - Method in interface deepnetts.data.MLDataItem: Returns an input for machine learning model of this item.
getInput() - Method in class deepnetts.data.TabularDataSet.Item
getInputLayer() - Method in class deepnetts.net.NeuralNetwork: Returns the input layer of this neural network.
getInstance() - Static method in class deepnetts.core.DeepNetts: Returns a singleton instance of a Deep Netts engine.The instance provides global configuration settings for the Deep Netts runtime.
getInt(String) - Method in class deepnetts.automl.Parameters
getInt(String) - Method in class deepnetts.util.TypedProperties: Get specified property as int.
getIntArray(String) - Method in class deepnetts.util.TypedProperties
getInteger(String) - Method in class deepnetts.util.Parameters
getInvertImages() - Method in class deepnetts.data.ImageSet
getIqr() - Method in class deepnetts.util.Stats
getL1RegSum() - Method in class deepnetts.net.NeuralNetwork: Calculates and returns L1 regularization sum of the entire network (all layers included).
getL1Regularization() - Method in class deepnetts.net.layers.AbstractLayer
getL1WeightSum() - Method in class deepnetts.net.layers.AbstractLayer
getL1WeightSum() - Method in class deepnetts.net.layers.ConvolutionalLayer
getL1WeightSum() - Method in class deepnetts.net.layers.MaxPoolingLayer
getL2RegSum() - Method in class deepnetts.net.NeuralNetwork: Calculates and returns L2 regularization sum of the entire network (all layers included).
getL2Regularization() - Method in class deepnetts.net.layers.AbstractLayer
getL2WeightSum() - Method in class deepnetts.net.layers.AbstractLayer
getL2WeightSum() - Method in class deepnetts.net.layers.ConvolutionalLayer
getL2WeightSum() - Method in class deepnetts.net.layers.MaxPoolingLayer
getLabel() - Method in class deepnetts.data.ExampleImage
getLabel() - Method in class deepnetts.net.NeuralNetwork: Returns the label(name) of this neural network
getLabel() - Method in class deepnetts.util.BoundingBox
getLabels() - Method in class deepnetts.util.LabelProbabilities
getLabelsCount() - Method in class deepnetts.data.ImageSet
getLayerAt(int) - Method in class deepnetts.net.NeuralNetwork
getLayers() - Method in class deepnetts.net.NeuralNetwork: Gets layers of this neural network.
getLayersOutputs() - Method in class deepnetts.net.ConvolutionalNetwork: Returns outputs of all layers.
getLayout() - Method in class deepnetts.cudnn.CudaTensorBridge
getLearningRate() - Method in class deepnetts.net.layers.AbstractLayer
getLearningRate() - Method in class deepnetts.net.train.BackpropagationTrainer: Learning rate controls the step size as a percent of the error to use for adjusting internal parameters(weights) of the neural network.
getLicenseProperties() - Method in class deepnetts.util.LicenseChecker
getLoss() - Method in class deepnetts.net.train.TrainingResult
getLossFunction() - Method in class deepnetts.net.NeuralNetwork: Returns a loss function of this network, which is used to calculate total network error during the training.
getLossType() - Method in class deepnetts.net.layers.OutputLayer
getMacroAverage() - Method in class deepnetts.eval.ClassifierEvaluator
getMacroAverage() - Method in class deepnetts.net.train.KFoldCrossValidation
getMatthewsCorrelationCoefficient() - Method in class deepnetts.eval.ClassificationMetrics: Calculates and returns the matthews corellation coefficient.
getMax() - Method in class deepnetts.automl.Range
getMax() - Method in class deepnetts.util.Stats
getMaxEpochs() - Method in class deepnetts.net.train.BackpropagationTrainer: Returns the setting for maximum number of training epochs(iterations).
getMaxError() - Method in class deepnetts.eval.RegressionMetrics
getMaxError() - Method in class deepnetts.net.train.BackpropagationTrainer: Returns the setting for the stopping error threshold.
getMaxInputs() - Method in class deepnetts.data.norm.MaxScaler
getMaxOutputs() - Method in class deepnetts.data.norm.MaxScaler
getMaxThreads() - Method in class deepnetts.core.DeepNetts
getMaxThreadsNum() - Static method in class deepnetts.util.DeepNettsThreadPool
getMean() - Method in class deepnetts.data.ImageSet
getMean() - Method in class deepnetts.util.ImagePreprocessing
getMean() - Method in class deepnetts.util.Stats
getMeanAbsoluteError() - Method in class deepnetts.eval.RegressionMetrics
getMeanAbsolutePercentageError() - Method in class deepnetts.eval.RegressionMetrics
getMeanSquaredError() - Method in class deepnetts.eval.RegressionMetrics: Mean squared error is the average value of the sum of squared errors.
getMedian() - Method in class deepnetts.util.Stats
getMetricsByClass() - Method in class deepnetts.eval.ClassifierEvaluator
getMin() - Method in class deepnetts.automl.Range
getMin() - Method in class deepnetts.util.Stats
getMode() - Method in class deepnetts.net.layers.AbstractLayer
getMode() - Method in class deepnetts.net.NeuralNetwork
getMomentum() - Method in class deepnetts.net.layers.AbstractLayer
getMomentum() - Method in class deepnetts.net.train.BackpropagationTrainer: Momentum settings helps to avoid oscillations in weight changes and get more stable and faster training.
getName() - Method in class deepnetts.automl.Parameter
getName() - Method in class deepnetts.util.Parameter
getNetwork() - Method in class deepnetts.automl.HyperParameterSearch
getNeuralNetwork() - Method in class deepnetts.net.train.BackpropagationTrainer: Returns a neural network trained by this trainer.
getNextLayer() - Method in class deepnetts.net.layers.AbstractLayer
getNormalizer() - Method in class deepnetts.net.NeuralNetwork: Returns data normalization method that is applied to network's inputs.
getNumColumns() - Method in class deepnetts.util.CsvFormat
getNumInputs() - Method in class deepnetts.data.TabularDataSet
getNumOutputs() - Method in class deepnetts.data.TabularDataSet
getOnDevPointer() - Method in class deepnetts.cudnn.CudaTensorBridge
getOptimizer() - Method in class deepnetts.net.layers.AbstractLayer
getOptimizer() - Method in class deepnetts.net.train.BackpropagationTrainer
getOptimizerType() - Method in class deepnetts.net.layers.AbstractLayer
getOrCreateAccBridge() - Method in class deepnetts.tensor.TensorBase
getOrCreateCudaBridge() - Method in class deepnetts.cudnn.CudaTensor
getOriginalImageFromFile() - Method in class deepnetts.data.ExampleImage
getOutput() - Method in class deepnetts.net.NeuralNetwork: Returns network's output.
getOutput(float[]) - Method in class deepnetts.net.FeedForwardNetwork: Deprecated.
getOutputErrors() - Method in class deepnetts.net.layers.OutputLayer
getOutputLabel(int) - Method in class deepnetts.net.NeuralNetwork: Gets a label of the i-th output this network.
getOutputLabels() - Method in class deepnetts.net.NeuralNetwork: Returns all labels for outputs of this network.
getOutputLayer() - Method in class deepnetts.net.NeuralNetwork: Returns the output layer of this network.
getOutputs() - Method in class deepnetts.net.layers.AbstractLayer
getOutputs() - Method in interface deepnetts.net.layers.Layer: Returns layer outputs (as a tensor).
getPadding() - Method in class deepnetts.net.layers.ConvolutionalLayer
getPadding() - Method in class deepnetts.net.layers.Filter
getParameters() - Method in class deepnetts.automl.HyperParameterSearch
getParameters() - Method in class deepnetts.automl.Parameters.ParameterCombination
getPatternLoss() - Method in class deepnetts.net.loss.BinaryCrossEntropyLoss
getPatternLoss() - Method in class deepnetts.net.loss.CrossEntropyLoss
getPatternLoss() - Method in interface deepnetts.net.loss.LossFunction
getPatternLoss() - Method in class deepnetts.net.loss.MeanSquaredErrorLoss
getPrecision() - Method in class deepnetts.eval.ClassificationMetrics: What percent of those predicted as positive are really positive.
getPreprocessing() - Method in class deepnetts.net.NeuralNetwork: Gets preprocessing that needs to be performed before input is fed to this network.
getPrevDeltaBiases() - Method in class deepnetts.net.layers.AbstractLayer
getPrevDeltaWeights() - Method in class deepnetts.net.layers.AbstractLayer
getPrevlayer() - Method in class deepnetts.net.layers.AbstractLayer
getPrime(float) - Method in interface deepnetts.net.layers.activation.ActivationFunction: Returns the first derivative of activation function for specified output y
getPrime(float) - Method in class deepnetts.net.layers.activation.LeakyRelu
getPrime(float) - Method in class deepnetts.net.layers.activation.Linear
getPrime(float) - Method in class deepnetts.net.layers.activation.Relu
getPrime(float) - Method in class deepnetts.net.layers.activation.Sigmoid
getPrime(float) - Method in class deepnetts.net.layers.activation.SoftSign: Note that this method accepts x as param not y as others
getPrime(float) - Method in class deepnetts.net.layers.activation.Tanh
getProbabilityOf(String) - Method in class deepnetts.util.LabelProbabilities
getProperties() - Method in class deepnetts.core.DeepNetts: Returns all the configuration properties for Deep Netts.
getProperties() - Method in class deepnetts.net.train.TrainingResult
getProperty(String) - Method in class deepnetts.core.DeepNetts: Returns a specified property of the Deep Netts runtime environment.
getQ1() - Method in class deepnetts.util.Stats
getQ3() - Method in class deepnetts.util.Stats
getR2() - Method in class deepnetts.eval.RegressionMetrics: Proportion of variance explained by the model.A value between 0 and 1, where 1 is the best and 0 worst.
getRandom() - Method in class deepnetts.util.RandomGenerator: Returns underlying Java random generator
getRandomNum() - Method in class deepnetts.automl.Range
getRange() - Method in class deepnetts.util.Stats
getRecall() - Method in class deepnetts.eval.ClassificationMetrics: Ratio between those classified as positive compared to those that are actually positive.
getRed(int) - Static method in class deepnetts.util.ColorUtils
getResidualStandardError() - Method in class deepnetts.eval.RegressionMetrics
getResizeStrategy() - Method in class deepnetts.data.ImageSet
getResults() - Method in class deepnetts.automl.HyperParameterSearch
getRgbVector() - Method in class deepnetts.data.ExampleImage
getRootMeanSquaredError() - Method in class deepnetts.eval.RegressionMetrics
getRow(int, float[]) - Method in class deepnetts.tensor.Tensor2D
getRowsCache(int) - Method in class deepnetts.tensor.Tensor2D
getScaleImages() - Method in class deepnetts.data.ImageSet: Returns flag that indicates wheather images should be scaled to specified dimensions while creating image set.
getScore() - Method in class deepnetts.util.BoundingBox
getSearchSpace() - Method in class deepnetts.automl.HyperParameterSearch
getShuffle() - Method in class deepnetts.net.train.BackpropagationTrainer: Returns shuffle flag which determines if training set should be shuffled before each epoch.
getSingleOutInput() - Method in class deepnetts.net.layers.OutputLayer
getSnapshotEpochs() - Method in class deepnetts.net.train.BackpropagationTrainer: On how many epochs to make training snapshots.
getSnapshotPath() - Method in class deepnetts.net.train.BackpropagationTrainer: Path to use for making snapshots - saving the current state of trained network during the training in order to be able to restore it from a training point if needed.
getSource() - Method in class deepnetts.net.train.TrainingEvent: Gets the source of the event.
getSpecificity() - Method in class deepnetts.eval.ClassificationMetrics: Specificity or true negative rate.
getSquaredErrorSum() - Method in class deepnetts.eval.RegressionMetrics
getStd() - Method in class deepnetts.util.Stats
getStep() - Method in class deepnetts.automl.Range
getStopAccuracy() - Method in class deepnetts.net.train.BackpropagationTrainer
getStopError() - Method in class deepnetts.net.train.BackpropagationTrainer: Alias for getMaxError().
getStrategy() - Method in class deepnetts.automl.Range
getStride() - Method in class deepnetts.net.layers.ConvolutionalLayer
getStride() - Method in class deepnetts.net.layers.Filter
getStride() - Method in class deepnetts.net.layers.MaxPoolingLayer
getString(String) - Method in class deepnetts.util.Parameters
getString(String) - Method in class deepnetts.util.TypedProperties: Get specified property as String .
getTargetColumnsNames() - Method in class deepnetts.data.ImageSet: Returns output/image labels.
getTargetColumnsNames() - Method in class deepnetts.data.TabularDataSet
getTargetOutput() - Method in class deepnetts.data.ExampleImage
getTargetOutput() - Method in interface deepnetts.data.MLDataItem: Returns target output for machine learning model of this item.
getTargetOutput() - Method in class deepnetts.data.TabularDataSet.Item
getTensor() - Method in class deepnetts.cudnn.CudaTensor
getTensor() - Method in class deepnetts.cudnn.CudaTensorBridge
getTensorDescriptor() - Method in class deepnetts.cudnn.CudaTensorBridge
getTensorDim() - Method in class deepnetts.net.layers.InputLayer
getTestSet() - Method in class deepnetts.data.TrainTestSplit
getTestSet() - Method in class deepnetts.net.train.BackpropagationTrainer: Test set is used after the training to estimate performance of the trained model and generalization ability with new data.
getThreadNum() - Method in class deepnetts.util.DeepNettsThreadPool
getThreadPool() - Method in class deepnetts.net.NeuralNetwork
getThreshold() - Method in class deepnetts.eval.ClassifierEvaluator
getTotal() - Method in class deepnetts.eval.ClassificationMetrics: Returns total number of classifications.
getTotal() - Method in class deepnetts.eval.RootMeanSquaredError
getTotal() - Method in class deepnetts.net.loss.BinaryCrossEntropyLoss
getTotal() - Method in class deepnetts.net.loss.CrossEntropyLoss
getTotal() - Method in interface deepnetts.net.loss.LossFunction: Returns the total error calculated by this loss function.
getTotal() - Method in class deepnetts.net.loss.MeanSquaredErrorLoss
getTotalItems() - Method in class deepnetts.eval.ConfusionMatrix
getTrainer() - Method in class deepnetts.net.NeuralNetwork: Returns a training algorithm of this neural network.
getTrainer() - Method in interface deepnetts.net.train.TrainerProvider
getTrainingAccuracy() - Method in class deepnetts.net.train.BackpropagationTrainer: Accuracy metric which tells us a percent of correct predictions for training set.
getTrainingListener() - Method in class deepnetts.net.train.KFoldCrossValidation
getTrainingLoss() - Method in class deepnetts.net.train.BackpropagationTrainer: Total training error/loss at the current epoch.
getTrainingResults() - Method in class deepnetts.net.train.KFoldCrossValidation
getTrainingSet() - Method in class deepnetts.automl.HyperParameterSearch
getTrainingSet() - Method in class deepnetts.data.TrainTestSplit
getTranslate() - Method in class deepnetts.data.ImageSet
getTransposed() - Method in class deepnetts.tensor.Tensor2D
getTrueNegative() - Method in class deepnetts.eval.ConfusionMatrix
getTrueNegative(int) - Method in class deepnetts.eval.ConfusionMatrix
getTruePositive() - Method in class deepnetts.eval.ConfusionMatrix: Return true positive metric for binary classification.
getTruePositive(int) - Method in class deepnetts.eval.ConfusionMatrix: Returns true positive metric for specified class idx for multiclass classification.
getType() - Method in class deepnetts.net.train.TrainingEvent: Gets the type of the event.
getValidationAccuracy() - Method in class deepnetts.net.train.BackpropagationTrainer: Accuracy metric which tells us a percent of correct predictions for validation set.
getValidationLoss() - Method in class deepnetts.net.train.BackpropagationTrainer: Validation loss is an error calculated using validation set, used to prevent overfitting, and validate architecture and training settings.
getValue() - Method in class deepnetts.automl.Parameter: If it does not hold list but a single value
getValue() - Method in class deepnetts.util.Parameter
getValue(float) - Method in interface deepnetts.net.layers.activation.ActivationFunction: Returns the value of activation function for specified input x
getValue(float) - Method in class deepnetts.net.layers.activation.LeakyRelu
getValue(float) - Method in class deepnetts.net.layers.activation.Linear
getValue(float) - Method in class deepnetts.net.layers.activation.Relu
getValue(float) - Method in class deepnetts.net.layers.activation.Sigmoid
getValue(float) - Method in class deepnetts.net.layers.activation.SoftSign
getValue(float) - Method in class deepnetts.net.layers.activation.Tanh
getValues() - Method in class deepnetts.automl.Parameter: Returns the list of possible values for this parameter
getValues() - Method in interface deepnetts.tensor.Tensor: Returns values stored in this tensor.
getValues() - Method in class deepnetts.tensor.TensorBase
getVar() - Method in class deepnetts.util.Stats
getWeights() - Method in class deepnetts.net.ConvolutionalNetwork: Returns weights from all layers in this network as a list of tensors.
getWeights() - Method in class deepnetts.net.layers.AbstractLayer
getWeights() - Method in class deepnetts.net.layers.ConvolutionalLayer
getWidth() - Method in class deepnetts.data.ExampleImage
getWidth() - Method in class deepnetts.net.layers.AbstractLayer
getWidth() - Method in class deepnetts.net.layers.Filter
getWidth() - Method in class deepnetts.util.BoundingBox
getXMin() - Method in class deepnetts.util.BoundingBox
getYCenter() - Method in class deepnetts.util.BoundingBox
getYMax() - Method in class deepnetts.util.BoundingBox
getYMin() - Method in class deepnetts.util.BoundingBox
getZeroMeanPixels() - Method in class deepnetts.data.ImageSet
grayscale(BufferedImage) - Static method in class deepnetts.util.ImageUtils: Returns grayscale version of the given image.
GRID - Enum constant in enum class deepnetts.automl.SearchStrategy
GRID - Static variable in class deepnetts.automl.HyperParameterSearch
groups(int) - Method in class deepnetts.net.layers.Filter

H

h - Variable in class deepnetts.cudnn.CudaTensorLayout
HALVING - Enum constant in enum class deepnetts.automl.SearchStrategy
handleEvent(TrainingEvent) - Method in class deepnetts.net.train.opt.AdaDeltaOptimizer
handleEvent(TrainingEvent) - Method in class deepnetts.net.train.opt.AdamOptimizer
handleEvent(TrainingEvent) - Method in class deepnetts.net.train.opt.LearningRateDecay
handleEvent(TrainingEvent) - Method in interface deepnetts.net.train.TrainingListener: Invoked when a training event occurs.
hashCode() - Method in class deepnetts.automl.Parameter
hashCode() - Method in class deepnetts.automl.Parameters.ParameterCombination
hashCode() - Method in class deepnetts.tensor.Shape
hashCode() - Method in class deepnetts.tensor.TensorBase
hasMissingValues() - Method in class deepnetts.data.TabularDataSet
hasMissingValues(int) - Method in class deepnetts.data.TabularDataSet
he(float[], int) - Static method in class deepnetts.net.weights.RandomWeights: He initialization, used for relu activations.zero-mean.
HE - Enum constant in enum class deepnetts.net.weights.RandomWeightsType
HIDDEN_ACTIVATION - Static variable in class deepnetts.automl.Parameters
HIDDEN_LAYERS - Static variable in class deepnetts.automl.Parameters
HIDDEN_NEURONS - Static variable in class deepnetts.automl.Parameters
hiddenActivationFunction(ActivationType) - Method in class deepnetts.net.ConvolutionalNetwork.Builder: Sets default type of the activation function to use for all hidden layers in the network.
hiddenActivationFunction(ActivationType) - Method in class deepnetts.net.FeedForwardNetwork.Builder: Sets default type of the activation function to use for all hidden layers in the network.
hIdx - Variable in class deepnetts.tensor.Layout
histogram(float[]) - Static method in class deepnetts.util.Statistics
histogram(float[], int) - Static method in class deepnetts.util.Statistics
histogram(float[], int, float, float) - Static method in class deepnetts.util.Statistics
Histogram() - Constructor for class deepnetts.util.Statistics.Histogram
HyperParameterSearch - Class in deepnetts.automl: The core automl class that performs automated model building and evaluation with specified parameters.
HyperParameterSearch() - Constructor for class deepnetts.automl.HyperParameterSearch

I

IMAGE_IDX_FILE - Static variable in class deepnetts.util.ImageSetUtils
ImagePreprocessing - Class in deepnetts.util: Image pre-processing to be used at inference time, after feeding input to the network.
ImagePreprocessing() - Constructor for class deepnetts.util.ImagePreprocessing
ImageResize - Enum Class in deepnetts.util
ImageSet - Class in deepnetts.data: Data set with images that will be used to train convolutional neural network.
ImageSet(int, int) - Constructor for class deepnetts.data.ImageSet: Creates empty image set for images of specified size.
ImageSet(int, int, String) - Constructor for class deepnetts.data.ImageSet: Creates image set with images from specified directory path.
ImageSetUtils - Class in deepnetts.util
ImageSetUtils() - Constructor for class deepnetts.util.ImageSetUtils
ImageUtils - Class in deepnetts.util: Utility methods to work with images.
importWeights(NeuralNetwork, String) - Static method in class deepnetts.util.TensorflowUtils
inc(int, int) - Method in class deepnetts.eval.ConfusionMatrix: Increments matrix value at specified position.
INFERENCE - Enum constant in enum class deepnetts.net.Mode
init() - Method in class deepnetts.net.layers.AbstractLayer: This method should implement layer initialization in subclasses, when a layer is added to the network (create weights, outputs, deltas, randomization etc.).
init() - Method in class deepnetts.net.layers.ConvolutionalLayer: Initialize dimensions, create output tensors, filters/weights, biases and all internal structures etc.
init() - Method in class deepnetts.net.layers.FlattenLayer
init() - Method in class deepnetts.net.layers.FullyConnectedLayer: Creates all internal data structures: inputs, weights, biases, outputs, deltas, deltaWeights, deltaBiases prevDeltaWeights, prevDeltaBiases.
init() - Method in class deepnetts.net.layers.InputLayer: Initialize this layer in network.
init() - Method in class deepnetts.net.layers.MaxPoolingLayer
init() - Method in class deepnetts.net.layers.OutputLayer
init() - Method in class deepnetts.net.layers.SoftmaxOutputLayer
initSeed(long) - Static method in class deepnetts.net.weights.RandomWeights: Initializes random number generator with specified seed.
initSeed(long) - Method in class deepnetts.util.RandomGenerator: Initialize the underlying Java random number generator with specified seed.
initTransientFields() - Method in class deepnetts.net.layers.AbstractLayer
initTransientFields() - Method in class deepnetts.net.layers.ConvolutionalLayer
initTransientFields() - Method in class deepnetts.net.layers.FlattenLayer
initTransientFields() - Method in class deepnetts.net.layers.FullyConnectedLayer
initTransientFields() - Method in class deepnetts.net.layers.MaxPoolingLayer
initTransientFields() - Method in class deepnetts.net.layers.SoftmaxOutputLayer
INPUT - Enum constant in enum class deepnetts.net.layers.LayerType: Accepts external input to the network.
InputLayer - Class in deepnetts.net.layers: Input layer in a neural network.
InputLayer(int) - Constructor for class deepnetts.net.layers.InputLayer: Creates input layer with specified width, and with height and depth equals to one.
InputLayer(int, int) - Constructor for class deepnetts.net.layers.InputLayer: Creates input layer with specified width and height, and depth=1 (single depth/channel).
InputLayer(int, int, int) - Constructor for class deepnetts.net.layers.InputLayer: Creates input layer with specified width, height, and depth (number of channels).
INPUTS - Static variable in class deepnetts.automl.Parameters
INTEGER - Enum constant in enum class deepnetts.util.ColumnType
invert() - Method in class deepnetts.data.ExampleImage
isAllocatedOnDev() - Method in class deepnetts.cudnn.CudaTensorBridge
isBatchMode() - Method in class deepnetts.net.layers.AbstractLayer
isBatchMode() - Method in class deepnetts.net.train.BackpropagationTrainer: In batch mode weights are adjusted after the pass of all examples from the training set, while in online mode weights are adjusted after each training example.
isEnabled() - Method in class deepnetts.util.ImagePreprocessing
isHasHeader() - Method in class deepnetts.util.CsvFormat
isImageFile(File) - Method in class deepnetts.util.AugmentImagesTest
isImageFile(File) - Method in class deepnetts.util.DeleteAugmentImages
isImageFile(File) - Method in class deepnetts.util.RandomlyTranslateImages
isInvertPixels() - Method in class deepnetts.util.ImagePreprocessing
isMultithreaded() - Method in class deepnetts.core.DeepNetts
isSubMean() - Method in class deepnetts.util.ImagePreprocessing
ISSUED_DATE - Static variable in class deepnetts.util.LicenseChecker
isTrainable() - Method in class deepnetts.net.layers.AbstractLayer
Item(float[], float[]) - Constructor for class deepnetts.data.TabularDataSet.Item
Item(TensorBase, TensorBase) - Constructor for class deepnetts.data.TabularDataSet.Item
ITERATION_FINISHED - Enum constant in enum class deepnetts.net.train.TrainingEvent.Type: Specifies a training iteration for (single data item) finished event
ITERATION_FINISHED - Static variable in class deepnetts.net.train.TrainingEvent: Specifies that training iteration has finished.

K

KFoldCrossValidation - Class in deepnetts.net.train: Split data set into k parts of equal sizes (folds), then train model with k-1 folds, and validate with remaining 1 fold.
KFoldCrossValidation.Builder - Class in deepnetts.net.train: Builder object for KFoldCrossValidation.

L

LabelProbabilities - Class in deepnetts.util
LabelProbabilities(Map<String, Float>) - Constructor for class deepnetts.util.LabelProbabilities
LABELS_FILE - Static variable in class deepnetts.util.ImageSetUtils
labelsFromSubDirectories(String) - Static method in class deepnetts.util.ImageSetUtils: Returns a list of category/class labels from the names of subdirectories for the given path.
Layer<O> - Interface in deepnetts.net.layers: Common base interface for all types of neural network layers.
LayerType - Enum Class in deepnetts.net.layers: Supported types of layers.
Layout - Class in deepnetts.tensor
Layout(int, int, int, int) - Constructor for class deepnetts.tensor.Layout
LEAKY_RELU - Enum constant in enum class deepnetts.net.layers.activation.ActivationType
LeakyRelu - Class in deepnetts.net.layers.activation: Leaky Rectified Linear Activation and its Derivative.
LeakyRelu() - Constructor for class deepnetts.net.layers.activation.LeakyRelu
LeakyRelu(float) - Constructor for class deepnetts.net.layers.activation.LeakyRelu
LEARNING_RATE - Static variable in class deepnetts.automl.Parameters
LearningRateDecay - Class in deepnetts.net.train.opt: https://www.coursera.org/learn/deep-neural-network/lecture/hjgIA/learning-rate-decay
LearningRateDecay(float, float) - Constructor for class deepnetts.net.train.opt.LearningRateDecay
LicenceException(String) - Constructor for exception class deepnetts.util.LicenseChecker.LicenceException
LICENSE_NUMBER - Static variable in class deepnetts.util.LicenseChecker
LICENSE_TYPE - Static variable in class deepnetts.util.LicenseChecker
LicenseChecker - Class in deepnetts.util: Provides methods that perform license file checks.
LicenseChecker() - Constructor for class deepnetts.util.LicenseChecker
LicenseChecker.LicenceException - Exception Class in deepnetts.util: This exception is thrown if there is some issue with the license file (invalid, expired or not found).
Linear - Class in deepnetts.net.layers.activation: Linear activation function and its derivative.
Linear() - Constructor for class deepnetts.net.layers.activation.Linear
Linear(int) - Constructor for class deepnetts.net.layers.activation.Linear
LINEAR - Enum constant in enum class deepnetts.net.layers.activation.ActivationType: Linear activation is used in output layer for regression tasks, or just passing input forward as it is.
listOf(Parameter...) - Static method in class deepnetts.util.Parameter
load(String, Class<T>) - Static method in class deepnetts.net.NeuralNetwork: Loads and returns neural network previously saved to a file.
loadFileImageMapFromDirectory(File) - Static method in class deepnetts.util.ImageUtils: Loads JPG, JPEG or PNG images from specified directory and returns them as a map with File object as a key and BufferedImage object as a value.
loadImages(File) - Method in class deepnetts.data.ImageSet: Loads example images with corresponding labels from the specified file.
loadImages(File, int) - Method in class deepnetts.data.ImageSet: Loads specified number of example images with corresponding labels from the specified file.
loadImages(String) - Method in class deepnetts.data.ImageSet: Loads images from the specified image index file.
loadImagesFromDirectory(File) - Static method in class deepnetts.util.ImageUtils: Loads all images from the specified directory, and returns them as a list.
loadLabels(File) - Method in class deepnetts.data.ImageSet: Loads and returns image labels to train neural network from the specified file.These labels will be used to label network's outputs.
loadLabels(String) - Method in class deepnetts.data.ImageSet: Loads and returns image labels to train neural network from the specified file.
LOGGER - Static variable in class deepnetts.core.DeepNetts
LOGGER - Static variable in class deepnetts.util.DeepNettsLogManager
lossFunction(LossType) - Method in class deepnetts.net.ConvolutionalNetwork.Builder: Sets loss function to be used by created neural network.
lossFunction(LossType) - Method in class deepnetts.net.FeedForwardNetwork.Builder: Sets loss function to be used by created neural network.
LossFunction - Interface in deepnetts.net.loss: Base Interface for all loss functions.
LossType - Enum Class in deepnetts.net.loss: Supported types of Loss Functions in Deep Netts engine.

M

macroAverage(Collection<EvaluationMetrics>) - Static method in class deepnetts.eval.ClassifierEvaluator: Calculates macro average for the given list of ClassificationMetrics.
macroAverage(Collection<EvaluationMetrics>) - Static method in class deepnetts.eval.RegresionEvaluator
main(String[]) - Static method in class deepnetts.util.AugmentImagesTest
main(String[]) - Static method in class deepnetts.util.CenterOnWhiteBackground
main(String[]) - Static method in class deepnetts.util.CreateImageIndex
main(String[]) - Static method in class deepnetts.util.CreateLabelsIndex
main(String[]) - Static method in class deepnetts.util.DeepNettsRunner
main(String[]) - Static method in class deepnetts.util.DeleteAugmentImages
main(String[]) - Static method in class deepnetts.util.GenerateRandomNegative
main(String[]) - Static method in class deepnetts.util.ObjectsOnBackgrounds
main(String[]) - Static method in class deepnetts.util.RandomlyTranslateImages
main(String[]) - Static method in class deepnetts.util.RunScaleImages
MathFunctions - Class in deepnetts.net.layers.activation: Misc math utility functions.
max() - Method in class deepnetts.util.Range
max(float[]) - Static method in class deepnetts.util.Statistics
max(TensorBase, TensorBase, TensorBase) - Static method in class deepnetts.tensor.Tensors
MAX_EPOCHS - Static variable in class deepnetts.automl.Parameters
MAX_ERROR - Static variable in class deepnetts.automl.Parameters
MAXPOOLING - Enum constant in enum class deepnetts.net.layers.LayerType: Downsizes the input.
MaxPoolingForwardCuda - Class in deepnetts.cudnn
MaxPoolingForwardCuda(CudaHandles, MaxPoolingLayer) - Constructor for class deepnetts.cudnn.MaxPoolingForwardCuda
MaxPoolingLayer - Class in deepnetts.net.layers: This layer performs max pooling operation in convolutional neural network, which scales down output from previous layer by taking max outputs from small predefined filter areas.
MaxPoolingLayer(int, int, int) - Constructor for class deepnetts.net.layers.MaxPoolingLayer: Creates a new max pooling layer with specified filter dimensions and stride.
MaxPoolingLayer(Filter) - Constructor for class deepnetts.net.layers.MaxPoolingLayer
MaxScaler - Class in deepnetts.data.norm: Performs max normalization, rescales data to corresponding max value in each column.
MaxScaler(DataSet<MLDataItem>) - Constructor for class deepnetts.data.norm.MaxScaler: Creates a new instance of max normalizer initialized to max values in given data set.
mean() - Method in class deepnetts.tensor.TensorBase
mean(float[]) - Static method in class deepnetts.util.Statistics
MEAN_SQUARED_ERROR - Enum constant in enum class deepnetts.net.loss.LossType: Mean Squared Error loss, used for regression tasks, implemented by MeanSquaredErrorLoss
MeanSquaredErrorLoss - Class in deepnetts.net.loss: Mean Squared Error Loss function.
MeanSquaredErrorLoss(int) - Constructor for class deepnetts.net.loss.MeanSquaredErrorLoss
MeanSquaredErrorLoss(NeuralNetwork) - Constructor for class deepnetts.net.loss.MeanSquaredErrorLoss: Creates a new mean squared error loss for the given neural network.
min() - Method in class deepnetts.util.Range
min(float[]) - Static method in class deepnetts.util.Statistics
min(TensorBase, TensorBase, TensorBase) - Static method in class deepnetts.tensor.Tensors
MINI_BATCH - Enum constant in enum class deepnetts.net.train.TrainingEvent.Type: Specifies a mini batch (batch of items) finished event
MINI_BATCH - Static variable in class deepnetts.net.train.TrainingEvent: Specifies mini batch event.
MinMaxScaler - Class in deepnetts.data.norm: Performs Min Max normalization on the given data set.
MinMaxScaler(DataSet<MLDataItem>) - Constructor for class deepnetts.data.norm.MinMaxScaler: Creates a new instance of max normalizer initialized to max values in given data set.
missingValuesCount(float[]) - Static method in class deepnetts.util.Statistics
missingValuesCount(float[][]) - Static method in class deepnetts.util.Statistics
missingValuesCount(TabularDataSet<TabularDataSet.Item>) - Static method in class deepnetts.util.Statistics
MLDataItem - Interface in deepnetts.data: Single data item that will be used to train machine learning model.
Mode - Enum Class in deepnetts.net
model(NeuralNetwork) - Method in class deepnetts.net.train.KFoldCrossValidation.Builder
MOMENTUM - Enum constant in enum class deepnetts.net.train.opt.OptimizerType
MOMENTUM - Static variable in class deepnetts.automl.Parameters
MomentumOptimizer - Class in deepnetts.net.train.opt: Momentum optimization adds momentum parameter to basic Stochastic Gradient Descent, which can accelerate the process.
MomentumOptimizer(AbstractLayer) - Constructor for class deepnetts.net.train.opt.MomentumOptimizer
mserror - Variable in class deepnetts.eval.ClassificationMetrics.Stats
multiply(float) - Method in class deepnetts.tensor.TensorBase: Multiplies all the values in tensor with a specified input parameter.
multiply(float[], float[]) - Static method in class deepnetts.tensor.Tensors
multiply(float, int) - Method in class deepnetts.tensor.TensorBase
multiplyElementWise(TensorBase) - Method in class deepnetts.tensor.TensorBase

N

n - Variable in class deepnetts.cudnn.CudaTensorLayout
NAME - Static variable in class deepnetts.util.LicenseChecker
names() - Method in class deepnetts.automl.Parameters
NCHW - Static variable in class deepnetts.tensor.Layout
negativeFreqency() - Method in class deepnetts.eval.ClassificationMetrics: How often does negative class actually occur in the sample
NETWORK_FILE_EXT - Static variable in class deepnetts.util.FileIO
networkFactory(NetworkFactory<?>) - Method in class deepnetts.automl.HyperParameterSearch
NetworkFactory<T> - Interface in deepnetts.automl: Base interface for all network factories.
NetworkType - Enum Class in deepnetts.net: Neural network architecture types.
NeuralNetwork<T> - Class in deepnetts.net: Base class for all neural networks in Deep Netts.
nextFloat() - Method in class deepnetts.util.RandomGenerator
nextGaussian() - Method in class deepnetts.util.RandomGenerator
nextInt() - Method in class deepnetts.util.RandomGenerator
NHWC - Static variable in class deepnetts.tensor.Layout
nIdx - Variable in class deepnetts.tensor.Layout
normal(float[]) - Static method in class deepnetts.net.weights.RandomWeights
normalizeInput(TensorBase) - Method in class deepnetts.data.norm.MaxScaler
numBins() - Method in class deepnetts.util.Statistics.Histogram
numDimensions() - Method in class deepnetts.tensor.Shape
numDimensions() - Method in interface deepnetts.tensor.Tensor
numDimensions() - Method in class deepnetts.tensor.TensorBase: Rank corresponds to number of dimensions in tensor.
numElements() - Method in class deepnetts.tensor.TensorBase: Total number of values in tensor.
numSplits(int) - Method in class deepnetts.net.train.KFoldCrossValidation.Builder

O

ObjectsOnBackgrounds - Class in deepnetts.util: Center images on backgounds and save at target path.
ObjectsOnBackgrounds() - Constructor for class deepnetts.util.ObjectsOnBackgrounds
of(float[]) - Static method in class deepnetts.tensor.Tensor1D
of(float, float) - Static method in class deepnetts.util.Range
of(int...) - Static method in class deepnetts.tensor.Shape
of(int, int, int, int) - Static method in class deepnetts.cudnn.CudaTensorLayout
of(Class) - Static method in enum class deepnetts.net.loss.LossType
of(T, T) - Static method in class deepnetts.automl.Range
of(T, T, T) - Static method in class deepnetts.automl.Range
Of(Class) - Static method in enum class deepnetts.net.NetworkType
ofSize(int) - Static method in class deepnetts.net.layers.Filters: Factory method that creates filter settings with specified size (using same size for filter width and height).
ofSize(int, int) - Static method in class deepnetts.net.layers.Filters: Factory method that creates a filter settings with specified width and height
oneHotEncode(String, String[]) - Static method in class deepnetts.data.DataSets: Returns one hot encoded vector for the given label.
ones(int) - Static method in class deepnetts.tensor.Tensors
Optimizer - Interface in deepnetts.net.train.opt: Optimization technique to tune network's weights parameters used by training algorithm.
OPTIMIZER - Static variable in class deepnetts.automl.Parameters
OptimizerType - Enum Class in deepnetts.net.train.opt: Supported types of optimization methods used by back-propagation training algorithm.
OUTPUT - Enum constant in enum class deepnetts.net.layers.LayerType: Provides final output of the network.
OUTPUT_ACTIVATION - Static variable in class deepnetts.automl.Parameters
OutputLayer - Class in deepnetts.net.layers: Output layer of a neural network.
OutputLayer(int) - Constructor for class deepnetts.net.layers.OutputLayer: Creates an instance of output layer with specified width (number of outputs) and sigmoid activation function by default.
OutputLayer(int, ActivationType) - Constructor for class deepnetts.net.layers.OutputLayer: Creates an instance of output layer with specified width (number of outputs) and specified activation function.
OutputLayer(String[], ActivationType) - Constructor for class deepnetts.net.layers.OutputLayer: Creates an instance of output layer with specified width (number of outputs) and linear activation function by default.
OUTPUTS - Static variable in class deepnetts.automl.Parameters

P

padding(int) - Method in class deepnetts.net.layers.Filter
Parameter<T> - Class in deepnetts.automl: A single parameter with name and all possible values to try.
Parameter<T> - Class in deepnetts.util
Parameter(String, List<T>) - Constructor for class deepnetts.automl.Parameter
Parameter(String, T) - Constructor for class deepnetts.util.Parameter
ParameterCombination() - Constructor for class deepnetts.automl.Parameters.ParameterCombination
ParameterCombination(List<Parameter>) - Constructor for class deepnetts.automl.Parameters.ParameterCombination
Parameters - Class in deepnetts.automl: Parameter search space: a collection of parameters and methods for generating all possible combinations.
Parameters - Class in deepnetts.util: TODO: setMethod, throw meannigfull exceptions
Parameters() - Constructor for class deepnetts.automl.Parameters
Parameters() - Constructor for class deepnetts.util.Parameters
Parameters.ParameterCombination - Class in deepnetts.automl: A single combination of parameters with list of parameters (with all possible values).
paramRange(String, Range<T>) - Method in class deepnetts.automl.HyperParameterSearch
paramValue(String, T) - Method in class deepnetts.automl.HyperParameterSearch: Set searvh param with specific value.
paramValues(String, List<T>) - Method in class deepnetts.automl.HyperParameterSearch
parseFloats(String) - Static method in class deepnetts.tensor.Tensors
parseInts(String) - Static method in class deepnetts.tensor.Tensors
physicalCoreCount() - Static method in class deepnetts.util.PhysicalCores: Returns the number of "physical" hardware threads available.
PhysicalCores - Class in deepnetts.util: Static utility class for finding the number of physical CPU cores.
positiveFreqency() - Method in class deepnetts.eval.ClassificationMetrics: How often does positive class actually occur in the sample
precision - Variable in class deepnetts.eval.ClassificationMetrics.Stats
predict(float...) - Method in class deepnetts.net.FeedForwardNetwork: Returns the network's prediction (outputs) for the given input.
predict(TensorBase) - Method in class deepnetts.net.NeuralNetwork: Returns the prediction of this neural network for the given input.
Preprocessing<T> - Interface in deepnetts.data: Data pre-processing abstraction.
printFloatDeviceVector(int, Pointer) - Static method in class deepnetts.cudnn.CudaUtils
printFloatDeviceVector(CudaTensor) - Static method in class deepnetts.cudnn.CudaUtils
printLicense() - Method in class deepnetts.util.LicenseChecker
printOnDevice() - Method in class deepnetts.cudnn.CudaTensorBridge
PROP_BATCH_MODE - Static variable in class deepnetts.net.train.BackpropagationTrainer: Name of the batchMode property
PROP_BATCH_SIZE - Static variable in class deepnetts.net.train.BackpropagationTrainer: Name of the batchSize property
PROP_IMAGE_HEIGHT - Static variable in class deepnetts.util.DeepNettsRunner
PROP_IMAGE_WIDTH - Static variable in class deepnetts.util.DeepNettsRunner
PROP_LABELS_FILE - Static variable in class deepnetts.util.DeepNettsRunner
PROP_LEARNING_RATE - Static variable in class deepnetts.net.train.BackpropagationTrainer: Name of the learningRate property
PROP_LEARNING_RATE - Static variable in class deepnetts.util.DeepNettsRunner
PROP_MAX_EPOCHS - Static variable in class deepnetts.net.train.BackpropagationTrainer: Name of the maxEpochs property
PROP_MAX_ERROR - Static variable in class deepnetts.net.train.BackpropagationTrainer: Name of the maxError property
PROP_MAX_ERROR - Static variable in class deepnetts.util.DeepNettsRunner
PROP_MOMENTUM - Static variable in class deepnetts.net.train.BackpropagationTrainer: Name of the momentum property
PROP_OPTIMIZER_TYPE - Static variable in class deepnetts.net.train.BackpropagationTrainer: Name of the optimizer property
PROP_TEST_FILE - Static variable in class deepnetts.util.DeepNettsRunner
PROP_TRAINING_FILE - Static variable in class deepnetts.util.DeepNettsRunner
put(Parameter<?>) - Method in class deepnetts.util.Parameters

R

random(int) - Static method in class deepnetts.tensor.Tensors: Generates a random 1D tensor with the specified dimensions.
random(int, int) - Static method in class deepnetts.tensor.Tensors: Create and return a tensor with specified number of rows and cols filled with random values.
random(int, int, int) - Static method in class deepnetts.tensor.Tensors
random(int, int, int, int) - Static method in class deepnetts.tensor.Tensors
RANDOM - Enum constant in enum class deepnetts.automl.SearchStrategy
RANDOM - Static variable in class deepnetts.automl.HyperParameterSearch
randomCrop(BufferedImage, int, int) - Static method in class deepnetts.util.ImageUtils: Randomly crop image with specified width and height.
randomCrop(BufferedImage, int, int, int, Random) - Static method in class deepnetts.util.ImageUtils: Crops specified number of random sub-images with specified dimensions.
RandomGenerator - Class in deepnetts.util: Random number generator singleton.
randomize() - Method in class deepnetts.tensor.TensorBase: Randomize all values in tensor
randomize(float[]) - Static method in class deepnetts.net.weights.RandomWeights: Initialize the elements of specified array with random numbers with uniform distribution in range [-0.5, 0.5].
RandomlyTranslateImages - Class in deepnetts.util: just move 2(x) pix to left right up down
RandomlyTranslateImages() - Constructor for class deepnetts.util.RandomlyTranslateImages
randomNum(int) - Method in class deepnetts.automl.Range
randomSeed(long) - Method in class deepnetts.automl.HyperParameterSearch
randomSeed(long) - Method in class deepnetts.net.ConvolutionalNetwork.Builder: Initializes random number generator with the specified seed in order to get same random number sequences used for weights initialization.
randomSeed(long) - Method in class deepnetts.net.FeedForwardNetwork.Builder: Initializes random number generator with the specified seed in order to get same random number sequences used for weights initialization.
randomTintAndBrightness(BufferedImage) - Static method in class deepnetts.util.ImageUtils
randomTintAndBrightness(BufferedImage, float, int, int, Random) - Static method in class deepnetts.util.ImageUtils: Still not working as it should
randomTranslateImage(BufferedImage, int, int) - Static method in class deepnetts.util.ImageUtils: Returns an array of images created by translating specified input image by specified number of count with specified step size.
RandomWeights - Class in deepnetts.net.weights: Weights randomization utility methods.
RandomWeights() - Constructor for class deepnetts.net.weights.RandomWeights
RandomWeightsType - Enum Class in deepnetts.net.weights: Types of supported weights randomization techniques.
range(float[]) - Static method in class deepnetts.util.Statistics
Range<T> - Class in deepnetts.automl: A value range of type T for the parameter.
Range - Class in deepnetts.util
Range(float, float) - Constructor for class deepnetts.util.Range
Range(T, T) - Constructor for class deepnetts.automl.Range
Range(T, T, T) - Constructor for class deepnetts.automl.Range
RangeConsumer - Interface in deepnetts.util
RangeScaler - Class in deepnetts.data.norm: Normalize data set to specified range.
RangeScaler(float, float) - Constructor for class deepnetts.data.norm.RangeScaler: Creates a new instance of range normalizer initialized to given min and max values.
readCsv(File, int, int, boolean, String) - Static method in class deepnetts.data.DataSets: Creates and returns data set from specified CSV file.
readCsv(String, int, int) - Static method in class deepnetts.data.DataSets: Create data set from CSV file, using coma (,) as default delimiter and no header (column names) in first row.
readCsv(String, int, int, boolean) - Static method in class deepnetts.data.DataSets
readCsv(String, int, int, boolean, String) - Static method in class deepnetts.data.DataSets
readCsv(String, int, int, String) - Static method in class deepnetts.data.DataSets
recall - Variable in class deepnetts.eval.ClassificationMetrics.Stats
RegresionEvaluator - Class in deepnetts.eval: Evaluates regressor neural network for specified data set.
RegresionEvaluator() - Constructor for class deepnetts.eval.RegresionEvaluator
RegressionMetrics - Class in deepnetts.eval: Common metrics for regression models.
RegressionMetrics() - Constructor for class deepnetts.eval.RegressionMetrics
Relu - Class in deepnetts.net.layers.activation: Rectified Linear Activation and its Derivative.
Relu() - Constructor for class deepnetts.net.layers.activation.Relu
RELU - Enum constant in enum class deepnetts.net.layers.activation.ActivationType
removeHandler(Handler) - Method in class deepnetts.util.DeepNettsLogManager
removeListener(TrainingListener) - Method in class deepnetts.net.train.BackpropagationTrainer: Removes training listener from this trainer.
renameFilesAsClasses(String, String) - Static method in class deepnetts.util.ImageSetUtils: Renames files in specified directory.
replace(float, float) - Method in class deepnetts.tensor.TensorBase
reset() - Method in class deepnetts.net.loss.BinaryCrossEntropyLoss
reset() - Method in class deepnetts.net.loss.CrossEntropyLoss
reset() - Method in interface deepnetts.net.loss.LossFunction: Resets the total error and pattern counter.
reset() - Method in class deepnetts.net.loss.MeanSquaredErrorLoss
RMSPROP - Enum constant in enum class deepnetts.net.train.opt.OptimizerType
RmsPropOptimizer - Class in deepnetts.net.train.opt: A variation of AdaDelta optimizer.
RmsPropOptimizer(AbstractLayer) - Constructor for class deepnetts.net.train.opt.RmsPropOptimizer
RootMeanSquaredError - Class in deepnetts.eval: A measure of error for regression tasks.
RootMeanSquaredError() - Constructor for class deepnetts.eval.RootMeanSquaredError
ROW_IDX - Static variable in interface deepnetts.net.train.opt.Optimizer
ROW_IDX - Static variable in class deepnetts.tensor.Shape
rows() - Method in class deepnetts.tensor.Tensor2D
rows() - Method in class deepnetts.tensor.Tensor3D
rows() - Method in class deepnetts.tensor.Tensor4D
run() - Method in class deepnetts.automl.HyperParameterSearch
run() - Method in class deepnetts.net.train.KFoldCrossValidation
run() - Method in class deepnetts.util.AugmentImagesTest
run() - Method in class deepnetts.util.DeleteAugmentImages
run() - Method in class deepnetts.util.RandomlyTranslateImages
run() - Method in class deepnetts.util.RunScaleImages
run(Runnable) - Method in class deepnetts.util.DeepNettsThreadPool
run(String) - Method in class deepnetts.util.DeepNettsRunner
run(Collection<Callable<Void>>) - Method in class deepnetts.util.DeepNettsThreadPool
RunScaleImages - Class in deepnetts.util
RunScaleImages() - Constructor for class deepnetts.util.RunScaleImages

S

save(String) - Method in class deepnetts.net.NeuralNetwork: Saves this network using serialization to file with specified fileName.
scaleAndCenter(BufferedImage, int, int, int, Color) - Static method in class deepnetts.util.ImageUtils: Scales input image to specified target width or height, centers and returns resulting image.
scaleBySmallerAndCrop(BufferedImage, int, int) - Static method in class deepnetts.util.ImageUtils: Scales input image to specified target width or height, crops and returns resulting image.
scaleBySmallerTarget(BufferedImage, int, int) - Static method in class deepnetts.util.ImageUtils
scaleImage(BufferedImage, int, int) - Static method in class deepnetts.util.ImageUtils: Scales specified image to given size and returns new image with specified width and height.
scaleInput(TensorBase) - Method in class deepnetts.data.norm.AbstractScaler: Normalize input of deployed model
scaleInput(TensorBase) - Method in class deepnetts.data.norm.DecimalScaler
scaleInput(TensorBase) - Method in class deepnetts.data.norm.MaxScaler
scaleInput(TensorBase) - Method in class deepnetts.data.norm.MinMaxScaler
scaleInput(TensorBase) - Method in class deepnetts.data.norm.RangeScaler
scaleInput(TensorBase) - Method in class deepnetts.data.norm.Standardizer
scaleToMax(DataSet) - Static method in class deepnetts.data.DataSets
scaleToMinMax(DataSet) - Static method in class deepnetts.data.DataSets
SearchStrategy - Enum Class in deepnetts.automl: Strategy for hyper-parameter search.
set(float, int) - Method in class deepnetts.tensor.Tensor1D: Sets value at specified index position.
set(float, int, int) - Method in class deepnetts.tensor.Tensor2D: Set value at specified [row, col] position.
set(float, int, int, int) - Method in class deepnetts.tensor.Tensor3D
set(float, int, int, int, int) - Method in class deepnetts.tensor.Tensor4D
setBatchMode(boolean) - Method in class deepnetts.net.layers.AbstractLayer
setBatchMode(boolean) - Method in class deepnetts.net.train.BackpropagationTrainer: Sets flag whether to use batch mode during the training.
setBatchSize(int) - Method in class deepnetts.net.layers.AbstractLayer
setBatchSize(int) - Method in class deepnetts.net.train.BackpropagationTrainer: Batch size is number of training examples after which network's weights are adjusted.
setBiases(Tensor1D) - Method in class deepnetts.net.layers.AbstractLayer
setBins(int[]) - Method in class deepnetts.util.Statistics.Histogram
setBinWidth(float) - Method in class deepnetts.util.Statistics.Histogram
setBrightness(boolean) - Method in class deepnetts.data.ImageSet
setCheckpointEpochs(int) - Method in class deepnetts.net.train.BackpropagationTrainer: On how many epochs the snapshots of the trained network should be created.
setClassLabel(String) - Method in class deepnetts.eval.ClassificationMetrics: Sets class label to which this metrics corresponds too
setCol(int, float[]) - Method in class deepnetts.tensor.Tensor2D
setColumnNames(String[]) - Method in class deepnetts.data.TabularDataSet
setColumnNames(String[]) - Method in class deepnetts.util.CsvFormat
setColumnTypes(ColumnType[]) - Method in class deepnetts.util.CsvFormat
setCount(int) - Method in class deepnetts.util.Stats
setCropCornersAndCenter(boolean) - Method in class deepnetts.data.ImageSet
setCudaHandles(CudaHandles) - Method in class deepnetts.net.layers.AbstractLayer
setDelimiter(String) - Method in class deepnetts.data.ImageSet
setDelimiter(String) - Method in class deepnetts.util.CsvFormat
setDeltas(O) - Method in class deepnetts.net.layers.AbstractLayer
setDropout(float) - Method in class deepnetts.net.layers.FullyConnectedLayer
setDropout(float) - Method in class deepnetts.net.train.BackpropagationTrainer: Dropout is a technique to prevent overfitting, which skips adjusting weights for some neurons with given probability.
setEarlyStopping(boolean) - Method in class deepnetts.net.train.BackpropagationTrainer: Early stopping stops training if it starts converging slow, and prevents overfitting.
setEarlyStoppingMinLossChange(float) - Method in class deepnetts.net.train.BackpropagationTrainer: Early stopping stops training if the error/loss start converging to slow.
setEarlyStoppingPatience(int) - Method in class deepnetts.net.train.BackpropagationTrainer: How many epochs to wait to see if the loss is lowering to slow.
setEnabled(boolean) - Method in class deepnetts.util.ImagePreprocessing
setError(float) - Method in class deepnetts.data.ExampleImage
setError(float) - Method in interface deepnetts.data.MLDataItem
setError(float) - Method in class deepnetts.data.TabularDataSet.Item
setExtendedLogging(boolean) - Method in class deepnetts.net.train.BackpropagationTrainer: Extended logging includes additional info for debugging the training.
setFileHandler(String) - Method in class deepnetts.util.DeepNettsLogManager
setFilters(Tensor4D) - Method in class deepnetts.net.layers.ConvolutionalLayer
setFilters(String) - Method in class deepnetts.net.layers.ConvolutionalLayer
setFlipHorizontal(boolean) - Method in class deepnetts.data.ImageSet
setFStat(float) - Method in class deepnetts.eval.RegressionMetrics
setGrayscale(boolean) - Method in class deepnetts.data.ImageSet
setHasHeader(boolean) - Method in class deepnetts.util.CsvFormat
setHiddenActivation(ActivationType) - Method in class deepnetts.automl.FeedForwardNetworkFactory
setId(int) - Method in class deepnetts.util.BoundingBox
setInput(float...) - Method in class deepnetts.net.FeedForwardNetwork: Sets network's input using given inputs and invokes the calculation of the network for the given input (forward pass).
setInput(TensorBase) - Method in class deepnetts.net.ConvolutionalNetwork
setInput(TensorBase) - Method in class deepnetts.net.layers.InputLayer: Sets network input
setInput(TensorBase) - Method in class deepnetts.net.NeuralNetwork: Sets network input and calculates entire network (triggers forward pass).
setInvertImages(boolean) - Method in class deepnetts.data.ImageSet
setInvertPixels(boolean) - Method in class deepnetts.util.ImagePreprocessing
setL1Regularization(float) - Method in class deepnetts.net.layers.AbstractLayer
setL1Regularization(float) - Method in class deepnetts.net.train.BackpropagationTrainer: L1 regularization (sum of abs values) is used to prevent overfitting and too large weights.
setL2Regularization(float) - Method in class deepnetts.net.layers.AbstractLayer
setL2Regularization(float) - Method in class deepnetts.net.train.BackpropagationTrainer: L2 regularization (sum of squares) is used to prevent overfitting and too large weights.
setLabel(String) - Method in class deepnetts.net.NeuralNetwork: Sets label(name) for this neural network.
setLabel(String) - Method in class deepnetts.util.BoundingBox
setLearningRate(float) - Method in class deepnetts.net.layers.AbstractLayer
setLearningRate(float) - Method in class deepnetts.net.train.BackpropagationTrainer: Learning rate controls the step size as a percent of the error to use for adjusting internal parameters(weights) of the neural network.
setLearningRate(float) - Method in class deepnetts.net.train.opt.AdaDeltaOptimizer
setLearningRate(float) - Method in class deepnetts.net.train.opt.AdaGradOptimizer
setLearningRate(float) - Method in class deepnetts.net.train.opt.AdamOptimizer
setLearningRate(float) - Method in class deepnetts.net.train.opt.MomentumOptimizer
setLearningRate(float) - Method in interface deepnetts.net.train.opt.Optimizer
setLearningRate(float) - Method in class deepnetts.net.train.opt.RmsPropOptimizer
setLearningRate(float) - Method in class deepnetts.net.train.opt.SgdOptimizer
setLearningRateDecay(float) - Method in class deepnetts.net.train.BackpropagationTrainer: Learning rate decay lowers the learning rate with each epoch by devayRate factor, which may improve error lowering the error.
setLossFunction(LossFunction) - Method in class deepnetts.net.NeuralNetwork: Sets a loss function of this network, which is used to calculate total network error during the training.
setLossType(LossType) - Method in class deepnetts.automl.FeedForwardNetworkFactory
setLossType(LossType) - Method in class deepnetts.net.layers.OutputLayer
setMax(float) - Method in class deepnetts.util.Stats
setMaxEpochs(long) - Method in class deepnetts.net.train.BackpropagationTrainer: Deprecated.
Use setStopEpochs instead
setMaxError(float) - Method in class deepnetts.eval.RegressionMetrics
setMaxError(float) - Method in class deepnetts.net.train.BackpropagationTrainer: Deprecated.
Use setStopError instead
setMaxInputs(TensorBase) - Method in class deepnetts.data.norm.MaxScaler
setMaxOutputs(TensorBase) - Method in class deepnetts.data.norm.MaxScaler
setMaxThreads(int) - Method in class deepnetts.core.DeepNetts
setMean(float) - Method in class deepnetts.util.Stats
setMean(TensorBase) - Method in class deepnetts.util.ImagePreprocessing
setMeanAbsoluteError(float) - Method in class deepnetts.eval.RegressionMetrics
setMeanAbsolutePercentageError(float) - Method in class deepnetts.eval.RegressionMetrics
setMeanSquaredError(float) - Method in class deepnetts.eval.RegressionMetrics
setMedian(float) - Method in class deepnetts.util.Stats
setMin(float) - Method in class deepnetts.util.Stats
setMode(Mode) - Method in class deepnetts.net.layers.AbstractLayer
setMode(Mode) - Method in class deepnetts.net.NeuralNetwork
setMomentum(float) - Method in class deepnetts.net.layers.AbstractLayer
setMomentum(float) - Method in class deepnetts.net.train.BackpropagationTrainer: Momentum settings helps to avoid oscillations in weight changes and get more stable and faster training.
setNextlayer(AbstractLayer) - Method in class deepnetts.net.layers.AbstractLayer
setNormalizer(AbstractScaler) - Method in class deepnetts.net.NeuralNetwork: Sets normalization data normalization method that is applied to network's inputs.
setNumBins(int) - Method in class deepnetts.util.Statistics.Histogram
setNumInputs(int) - Method in class deepnetts.automl.FeedForwardNetworkFactory
setNumOutputs(int) - Method in class deepnetts.automl.FeedForwardNetworkFactory
setOptimizer(OptimizerType) - Method in class deepnetts.net.train.BackpropagationTrainer
setOptimizerType(OptimizerType) - Method in class deepnetts.net.layers.AbstractLayer
setOptimizerType(OptimizerType) - Method in class deepnetts.net.layers.FlattenLayer
setOutputActivation(ActivationType) - Method in class deepnetts.automl.FeedForwardNetworkFactory
setOutputError(TensorBase) - Method in class deepnetts.net.NeuralNetwork: Sets the network's output errors, which are a difference between actual(predicted) and target output.
setOutputErrors(TensorBase) - Method in class deepnetts.net.layers.OutputLayer
setOutputLabels(String...) - Method in class deepnetts.net.NeuralNetwork: Sets output labels of this network.
setOutputs(O) - Method in class deepnetts.net.layers.AbstractLayer
setPreprocessing(Preprocessing<TensorBase>) - Method in class deepnetts.net.NeuralNetwork: Sets preprocessing that needs to be performed before input is fed to this network.
setPrevDeltaWeights(W) - Method in class deepnetts.net.layers.AbstractLayer
setPrevLayer(AbstractLayer) - Method in class deepnetts.net.layers.AbstractLayer
setProperties(Properties) - Method in class deepnetts.net.train.BackpropagationTrainer: Sets properties from available keys in specified prop object.
setQ1(float) - Method in class deepnetts.util.Stats
setQ3(float) - Method in class deepnetts.util.Stats
setR2(float) - Method in class deepnetts.eval.RegressionMetrics
setResidualStandardError(float) - Method in class deepnetts.eval.RegressionMetrics
setResizeStrategy(ImageResize) - Method in class deepnetts.data.ImageSet
setRootMeanSquaredError(float) - Method in class deepnetts.eval.RegressionMetrics
setRow(float[], int, int) - Method in class deepnetts.tensor.Tensor3D
setRow(int, float[]) - Method in class deepnetts.tensor.Tensor2D
setScaleImages(boolean) - Method in class deepnetts.data.ImageSet
setScore(float) - Method in class deepnetts.util.BoundingBox
setShuffle(boolean) - Method in class deepnetts.net.train.BackpropagationTrainer: Sets shuffle flag which determines if training set should be shuffled before each epoch.
setSnapshotEpochs(int) - Method in class deepnetts.net.train.BackpropagationTrainer: On how many epochs to make training snapshots.
setSnapshotPath(String) - Method in class deepnetts.net.train.BackpropagationTrainer: Path to use for making snapshots - saving the current state of trained network during the training in order to be able to restore it from a training point.
setSquaredErrorSum(float) - Method in class deepnetts.eval.RegressionMetrics
setStd(float) - Method in class deepnetts.util.Stats
setStopAccuracy(float) - Method in class deepnetts.net.train.BackpropagationTrainer
setStopEpochs(long) - Method in class deepnetts.net.train.BackpropagationTrainer: Sets number of epochs/iterations to run the training.
setStopError(float) - Method in class deepnetts.net.train.BackpropagationTrainer: The training stops when/if training error reach this value.
setSubMean(boolean) - Method in class deepnetts.util.ImagePreprocessing
setTargetOutput(TensorBase) - Method in class deepnetts.data.ExampleImage
setTensorDesc(cudnnTensorDescriptor, int, int, CudaTensorLayout) - Method in class deepnetts.cudnn.CudaTensorBridge
setTestSet(DataSet<MLDataItem>) - Method in class deepnetts.net.train.BackpropagationTrainer: Test set is used after the training to estimate performance of the trained model and generalization ability with new data.
setThreadNum(int) - Method in class deepnetts.util.DeepNettsThreadPool
setThreadPool(DeepNettsThreadPool) - Method in class deepnetts.net.layers.AbstractLayer
setThreshold(float) - Method in class deepnetts.eval.ClassifierEvaluator
setTrainable(boolean) - Method in class deepnetts.net.layers.AbstractLayer: Set trainable to false to freeze learned weights.
setTrainer(T) - Method in class deepnetts.net.NeuralNetwork: Sets the training algorithm of this neural network.
setTrainer(T) - Method in interface deepnetts.net.train.TrainerProvider
setTrainingSnapshots(boolean) - Method in class deepnetts.net.train.BackpropagationTrainer: Training snapshots save the current state of the trained neural network during the training in order to be able to restore it from a training point if needed.
setTranslate(boolean) - Method in class deepnetts.data.ImageSet
setUseCuda(boolean) - Method in class deepnetts.core.DeepNetts
setValues(float...) - Method in class deepnetts.tensor.TensorBase
setValuesFromString(String) - Method in class deepnetts.tensor.TensorBase: Sets tensor values from CSV string.
setValuesFromStringTransposed(String) - Method in class deepnetts.tensor.Tensor2D
setValuesFromStringTransposed(String) - Method in class deepnetts.tensor.Tensor4D
setValuesFromStringTransposed(String) - Method in class deepnetts.tensor.TensorBase
setVar(float) - Method in class deepnetts.util.Stats
setWeights(String) - Method in class deepnetts.net.layers.AbstractLayer
setWeights(List<String>) - Method in class deepnetts.net.ConvolutionalNetwork: Sets network's weights for all layers.
setWeights(W) - Method in class deepnetts.net.layers.AbstractLayer
setXMax(int) - Method in class deepnetts.util.BoundingBox
setXMin(int) - Method in class deepnetts.util.BoundingBox
setYCenter(int) - Method in class deepnetts.util.BoundingBox
setYMax(int) - Method in class deepnetts.util.BoundingBox
setYMin(int) - Method in class deepnetts.util.BoundingBox
SGD - Enum constant in enum class deepnetts.net.train.opt.OptimizerType: Stochastic Gradient Descent, a basic type of neural network optimization algorithm.
SgdOptimizer - Class in deepnetts.net.train.opt: Basic Stochastic Gradient Descent optimization algorithm, which iteratively change weights towards value which gives minimum error.
SgdOptimizer(AbstractLayer) - Constructor for class deepnetts.net.train.opt.SgdOptimizer
shape() - Method in interface deepnetts.tensor.Tensor: Returns the shape of this tensor.
shape() - Method in class deepnetts.tensor.TensorBase
Shape - Class in deepnetts.tensor: Immutable class that represents Tensor shape.
Shape(int...) - Constructor for class deepnetts.tensor.Shape
shuffle() - Method in class deepnetts.data.TabularDataSet: Shuffles the data set items using the default random generator.
shuffle(int) - Method in class deepnetts.data.TabularDataSet: Shuffles data set items using java random generator initializes with specified seed
shutdown() - Method in class deepnetts.util.DeepNettsThreadPool
shutdownNow() - Method in class deepnetts.util.DeepNettsThreadPool
Sigmoid - Class in deepnetts.net.layers.activation: Sigmoid activation function
Sigmoid() - Constructor for class deepnetts.net.layers.activation.Sigmoid
SIGMOID - Enum constant in enum class deepnetts.net.layers.activation.ActivationType: Sigmoid activation is used in output layer for binary classification tasks and logistic regression.
SOFTMAX - Enum constant in enum class deepnetts.net.layers.activation.ActivationType: SoftMax activation is typically used in output layer, for multi class classification tasks.
SoftMaxBackwardCuda - Class in deepnetts.cudnn
SoftMaxBackwardCuda(CudaHandles, SoftmaxOutputLayer) - Constructor for class deepnetts.cudnn.SoftMaxBackwardCuda
SoftMaxBackwardCuda(CudaHandles, TensorBase, TensorBase, TensorBase, TensorBase) - Constructor for class deepnetts.cudnn.SoftMaxBackwardCuda
SoftMaxForwardCuda - Class in deepnetts.cudnn
SoftMaxForwardCuda(CudaHandles, SoftmaxOutputLayer) - Constructor for class deepnetts.cudnn.SoftMaxForwardCuda
SoftMaxForwardCuda(CudaHandles, TensorBase, TensorBase, TensorBase, TensorBase) - Constructor for class deepnetts.cudnn.SoftMaxForwardCuda
SoftmaxOutputLayer - Class in deepnetts.net.layers: Output layer with softmax activation function.
SoftmaxOutputLayer(int) - Constructor for class deepnetts.net.layers.SoftmaxOutputLayer
SoftSign - Class in deepnetts.net.layers.activation: Soft sign activation function
SoftSign() - Constructor for class deepnetts.net.layers.activation.SoftSign
split(double...) - Method in class deepnetts.data.ImageSet: Splits data set into several parts specified by the input parameter partSizes.
split(double...) - Method in class deepnetts.data.TabularDataSet: Splits data set into several parts specified by the input parameter partSizes.
split(int) - Method in class deepnetts.data.TabularDataSet: Split data set into specified number of part of equal sizes.
sqr() - Method in class deepnetts.tensor.TensorBase
sqrt() - Method in class deepnetts.tensor.TensorBase
standardDeviation(float[], float) - Static method in class deepnetts.util.Statistics
Standardizer - Class in deepnetts.data.norm: Performs standardization on inputs in order to get desired statistical properties of the data set (zero mean and one standard deviation).
Standardizer(DataSet<MLDataItem>) - Constructor for class deepnetts.data.norm.Standardizer
STARTED - Enum constant in enum class deepnetts.net.train.TrainingEvent.Type: Specifies a training started event
STARTED - Static variable in class deepnetts.net.train.TrainingEvent: Specifies that training has started.
Statistics - Class in deepnetts.util: Statistical functions.
Statistics() - Constructor for class deepnetts.util.Statistics
Statistics.Histogram - Class in deepnetts.util
Stats - Class in deepnetts.util
Stats() - Constructor for class deepnetts.eval.ClassificationMetrics.Stats
Stats() - Constructor for class deepnetts.util.Stats
std(float) - Method in class deepnetts.tensor.TensorBase
step(T) - Method in class deepnetts.automl.Range
stop() - Method in class deepnetts.net.train.BackpropagationTrainer: Stops the training.
STOP_EPOCHS - Static variable in class deepnetts.automl.Parameters
STOP_ERROR - Static variable in class deepnetts.automl.Parameters
STOPPED - Enum constant in enum class deepnetts.net.train.TrainingEvent.Type: Specifies a training stopped event
STOPPED - Static variable in class deepnetts.net.train.TrainingEvent: Specifies that training has stopped.
STRATCH - Enum constant in enum class deepnetts.util.ImageResize
strategy(String) - Method in class deepnetts.automl.Range
stride(int) - Method in class deepnetts.net.layers.Filter
STRING - Enum constant in enum class deepnetts.util.ColumnType
sub(float) - Method in class deepnetts.tensor.TensorBase
sub(float[], float) - Static method in class deepnetts.tensor.Tensors
sub(float[], float[]) - Static method in class deepnetts.tensor.Tensors
sub(float, int, int) - Method in class deepnetts.tensor.Tensor2D
sub(float, int, int, int) - Method in class deepnetts.tensor.Tensor3D
sub(float, int, int, int, int) - Method in class deepnetts.tensor.Tensor4D
sub(TensorBase) - Method in class deepnetts.tensor.TensorBase: Subtracts specified tensor t from this tensor.
subInplace(TensorBase, TensorBase) - Static method in class deepnetts.tensor.TensorBase: Subtracts tensor t2 from t1.
subInto(TensorBase, TensorBase) - Method in class deepnetts.tensor.TensorBase
submit(Callable<?>) - Method in class deepnetts.util.DeepNettsThreadPool: Submit a single task to thread pool.
sum() - Method in class deepnetts.tensor.TensorBase
sumAbs() - Method in class deepnetts.tensor.TensorBase: Returns sum of abs values of this tensor - L1 norm
sumSqr() - Method in class deepnetts.tensor.TensorBase: Returns sum of squared values of this tensor - L2 norm

T

TabularDataSet<T> - Class in deepnetts.data: Basic data set with tabular data.
TabularDataSet(int, int) - Constructor for class deepnetts.data.TabularDataSet: Create a new instance of BasicDataSet with specified size of input and output.
TabularDataSet.Item - Class in deepnetts.data: Represents a basic data set item (single row) with input tensor and target vector in a data set.
Tanh - Class in deepnetts.net.layers.activation: Hyperbolic tangens activation function
Tanh() - Constructor for class deepnetts.net.layers.activation.Tanh
TANH - Enum constant in enum class deepnetts.net.layers.activation.ActivationType
Tensor - Interface in deepnetts.tensor
Tensor1D - Class in deepnetts.tensor: One dimensional tensor - a vector or an array.
Tensor1D(float...) - Constructor for class deepnetts.tensor.Tensor1D: Creates a single row tensor with specified values.
Tensor1D(int) - Constructor for class deepnetts.tensor.Tensor1D
Tensor1D(Shape, float[]) - Constructor for class deepnetts.tensor.Tensor1D
Tensor2D - Class in deepnetts.tensor: A 2D tensor / matrix with specified number of rows and columns..
Tensor2D(float[][]) - Constructor for class deepnetts.tensor.Tensor2D: Creates a 2D tensor / matrix with specified values.
Tensor2D(int, int) - Constructor for class deepnetts.tensor.Tensor2D: Creates a tensor with specified number of rows and columns (matrix).
Tensor2D(int, int, float[]) - Constructor for class deepnetts.tensor.Tensor2D
Tensor3D - Class in deepnetts.tensor
Tensor3D(float[][][]) - Constructor for class deepnetts.tensor.Tensor3D: Creates a 3D tensor from specified 3D array
Tensor3D(int, int, int) - Constructor for class deepnetts.tensor.Tensor3D: Creates a 3D tensor with specified number of rows, cols and depth.
Tensor3D(int, int, int, float[]) - Constructor for class deepnetts.tensor.Tensor3D
Tensor4D - Class in deepnetts.tensor
Tensor4D(float[][][][]) - Constructor for class deepnetts.tensor.Tensor4D
Tensor4D(int, int, int, int) - Constructor for class deepnetts.tensor.Tensor4D
Tensor4D(int, int, int, int, float[]) - Constructor for class deepnetts.tensor.Tensor4D
TensorAcceleratorBridge - Interface in deepnetts.util
TensorBase - Class in deepnetts.tensor: This class represents a wrapper for multidimensional array.
TensorBase(Shape, float...) - Constructor for class deepnetts.tensor.TensorBase
TensorBase(TensorBase) - Constructor for class deepnetts.tensor.TensorBase: Public deep copy / clone constructor.
TensorflowUtils - Class in deepnetts.util
TensorflowUtils() - Constructor for class deepnetts.util.TensorflowUtils
Tensors - Class in deepnetts.tensor: Static utility methods for tensors.
test(DataSet<? extends MLDataItem>) - Method in class deepnetts.net.NeuralNetwork: Tests how good are predictions of this network using specified test set.
TEST_FILE - Static variable in class deepnetts.util.ImageSetUtils
testSet(DataSet) - Method in class deepnetts.automl.HyperParameterSearch
toJson(NeuralNetwork<?>) - Static method in class deepnetts.util.FileIO: Returns JSON representation of specified neural network object.
toString() - Method in class deepnetts.automl.Parameter
toString() - Method in class deepnetts.automl.Parameters.ParameterCombination
toString() - Method in class deepnetts.automl.Parameters
toString() - Method in class deepnetts.data.TabularDataSet.Item
toString() - Method in class deepnetts.eval.ClassificationMetrics.Stats
toString() - Method in class deepnetts.eval.ClassificationMetrics
toString() - Method in class deepnetts.eval.ClassifierEvaluator
toString() - Method in class deepnetts.eval.ConfusionMatrix
toString() - Method in class deepnetts.eval.RegressionMetrics
toString() - Method in class deepnetts.net.layers.ConvolutionalLayer
toString() - Method in class deepnetts.net.layers.FlattenLayer
toString() - Method in class deepnetts.net.layers.FullyConnectedLayer
toString() - Method in class deepnetts.net.layers.InputLayer
toString() - Method in enum class deepnetts.net.layers.LayerType
toString() - Method in class deepnetts.net.layers.MaxPoolingLayer
toString() - Method in class deepnetts.net.layers.OutputLayer
toString() - Method in enum class deepnetts.net.loss.LossType
toString() - Method in enum class deepnetts.net.NetworkType
toString() - Method in class deepnetts.net.NeuralNetwork: Returns string representation of this network including all layers and settings.
toString() - Method in class deepnetts.net.train.TrainingResult
toString() - Method in class deepnetts.tensor.Shape
toString() - Method in class deepnetts.tensor.Tensor2D
toString() - Method in class deepnetts.tensor.Tensor3D
toString() - Method in class deepnetts.tensor.TensorBase
toString() - Method in class deepnetts.util.BoundingBox
toString() - Method in class deepnetts.util.CsvFormat
toString() - Method in class deepnetts.util.LabelProbabilities
toString() - Method in class deepnetts.util.Parameter
toString() - Method in class deepnetts.util.Stats
train(DataSet<?>, double) - Method in class deepnetts.net.train.BackpropagationTrainer: Run training using given training set, and split part of it to use as a validation set.
train(DataSet<? extends MLDataItem>) - Method in class deepnetts.net.NeuralNetwork: Trains the neural network using specified training set.
train(DataSet<? extends MLDataItem>) - Method in class deepnetts.net.train.BackpropagationTrainer: Runs training using specified training set.
train(DataSet<? extends MLDataItem>) - Method in interface deepnetts.net.train.Trainer: Trains this model using specified training set.
train(DataSet<MLDataItem>, DataSet<MLDataItem>) - Method in class deepnetts.net.train.BackpropagationTrainer: Runs training using given training and validation sets.
TRAIN - Enum constant in enum class deepnetts.net.Mode
TRAIN_FILE - Static variable in class deepnetts.util.ImageSetUtils
Trainer - Interface in deepnetts.net.train: Generic interface for deep learning training algorithm.
TrainerProvider<T> - Interface in deepnetts.net.train: This interface is implemented by trainable deep learning models, in order to provide access to training algorithm.
TrainingEvent - Class in deepnetts.net.train: TrainingEvent is used to notify interested parties that training event has happened.
TrainingEvent(BackpropagationTrainer, TrainingEvent.Type) - Constructor for class deepnetts.net.train.TrainingEvent: Constructs a new TrainingEvent with specified source and type.
TrainingEvent.Type - Enum Class in deepnetts.net.train: Type of a training event.
trainingListener(TrainingListener) - Method in class deepnetts.automl.HyperParameterSearch
trainingListener(TrainingListener) - Method in class deepnetts.net.train.KFoldCrossValidation.Builder
TrainingListener - Interface in deepnetts.net.train: The listener interface for receiving notifications about training events.
TrainingResult - Class in deepnetts.net.train: All information about the completed training including training settings, epochs, loss and evaluation metrics.
TrainingResult(Properties, int, float, EvaluationMetrics) - Constructor for class deepnetts.net.train.TrainingResult
trainingSet(DataSet) - Method in class deepnetts.automl.HyperParameterSearch
trainingSet(DataSet) - Method in class deepnetts.net.train.KFoldCrossValidation.Builder
trainTestSplit(double) - Method in class deepnetts.data.TabularDataSet
trainTestSplit(DataSet<?>, double) - Static method in class deepnetts.data.DataSets
TrainTestSplit - Class in deepnetts.data: This class holds training and test data set pair.
TrainTestSplit(DataSet<MLDataItem>, DataSet<MLDataItem>) - Constructor for class deepnetts.data.TrainTestSplit
translateImage(BufferedImage) - Static method in class deepnetts.util.ImageUtils: Generates 4 translated variations of the given image: translates it for one third of an image width and height in each direction.
TRUE_NEGATIVE - Static variable in class deepnetts.eval.ConfusionMatrix: A label for items classified as negative which are really negative.
TRUE_POSITIVE - Static variable in class deepnetts.eval.ConfusionMatrix: A label for items classified as positive which are really positive.
TypedProperties - Class in deepnetts.util: Provides methods for getting typed properties for the given key.
TypedProperties(Properties) - Constructor for class deepnetts.util.TypedProperties

U

uniform(float[], float, float) - Static method in class deepnetts.net.weights.RandomWeights
uniform(float[], int) - Static method in class deepnetts.net.weights.RandomWeights: Uniform U[-a,a] where a=1/sqrt(in).
UNIFORM - Enum constant in enum class deepnetts.net.weights.RandomWeightsType
updateLearningRate(float) - Method in class deepnetts.net.train.BackpropagationTrainer: Updates learning rate for all layers during the learning rate decay.
useCuda() - Method in class deepnetts.core.DeepNetts

V

VALID_UNTIL_DATE - Static variable in class deepnetts.util.LicenseChecker
valueFor(NeuralNetwork, DataSet<? extends MLDataItem>) - Method in interface deepnetts.net.loss.LossFunction: Calculates and returns loss function value for the given neural network and data set.
valueOf(String) - Static method in enum class deepnetts.automl.SearchStrategy: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class deepnetts.net.layers.activation.ActivationType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class deepnetts.net.layers.LayerType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class deepnetts.net.loss.LossType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class deepnetts.net.Mode: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class deepnetts.net.NetworkType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class deepnetts.net.train.opt.OptimizerType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class deepnetts.net.train.TrainingEvent.Type: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class deepnetts.net.weights.RandomWeightsType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class deepnetts.util.ColumnType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class deepnetts.util.ImageResize: Returns the enum constant of this class with the specified name.
values() - Method in class deepnetts.automl.Parameters
values() - Static method in enum class deepnetts.automl.SearchStrategy: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class deepnetts.net.layers.activation.ActivationType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class deepnetts.net.layers.LayerType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class deepnetts.net.loss.LossType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class deepnetts.net.Mode: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class deepnetts.net.NetworkType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class deepnetts.net.train.opt.OptimizerType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class deepnetts.net.train.TrainingEvent.Type: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class deepnetts.net.weights.RandomWeightsType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class deepnetts.util.ColumnType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class deepnetts.util.ImageResize: Returns an array containing the constants of this enum class, in the order they are declared.
valuesAsString(TensorBase[]) - Static method in class deepnetts.tensor.TensorBase
variance(float[], float) - Static method in class deepnetts.util.Statistics
version() - Method in class deepnetts.core.DeepNetts: Returns the Deep Netts version.

W

w - Variable in class deepnetts.cudnn.CudaTensorLayout
WIDDROW_HOFF - Enum constant in enum class deepnetts.net.weights.RandomWeightsType
widrowHoff(float[], float, float) - Static method in class deepnetts.net.weights.RandomWeights
wIdx - Variable in class deepnetts.tensor.Layout
writeImages(List<BufferedImage>, String, String, String) - Static method in class deepnetts.util.ImageUtils: Writes list of images to specified file path.
writeToFile(NeuralNetwork, String) - Static method in class deepnetts.util.FileIO: Serializes specified neural network to file with specified file.
writeToFile(List<String>, String) - Static method in class deepnetts.util.ImageSetUtils: Writes a given list of strings to file.
writeToFileAsJson(NeuralNetwork, String) - Static method in class deepnetts.util.FileIO

X

xavier(float[], int, int) - Static method in class deepnetts.net.weights.RandomWeights: Normalized uniform initialization U[-a,a] with a = sqrt(6/(in + out)).
XAVIER - Enum constant in enum class deepnetts.net.weights.RandomWeightsType

Z

zeroMean() - Method in class deepnetts.data.ImageSet: Applies zero mean normalization to entire dataset, and returns mean tensor.
zeroMeanAndNormalize() - Method in class deepnetts.data.ImageSet
zeroMeanPerChannel() - Method in class deepnetts.data.ImageSet
zeros(int) - Static method in class deepnetts.tensor.Tensors

A B C D E F G H I K L M N O P R S T U V W X Z
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